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overflow: hidden; text-overflow: ellipsis; -webkit-line-clamp: 3; -webkit-box-orient: vertical; }</style><div class="col-xs-12 clearfix"><div class="u-floatLeft"><h1 class="PageHeader-title u-m0x u-fs30">Natural Language Processing</h1><div class="u-tcGrayDark">236,328 Followers</div><div class="u-tcGrayDark u-mt2x">Recent papers in <b>Natural Language Processing</b></div></div></div></div></div></div><div class="TabbedNavigation"><div class="container"><div class="row"><div class="col-xs-12 clearfix"><ul class="nav u-m0x u-p0x list-inline u-displayFlex"><li class="active"><a href="https://www.academia.edu/Documents/in/Natural_Language_Processing">Top Papers</a></li><li><a href="https://www.academia.edu/Documents/in/Natural_Language_Processing/MostCited">Most Cited Papers</a></li><li><a href="https://www.academia.edu/Documents/in/Natural_Language_Processing/MostDownloaded">Most Downloaded Papers</a></li><li><a href="https://www.academia.edu/Documents/in/Natural_Language_Processing/MostRecent">Newest Papers</a></li><li><a class="" href="https://www.academia.edu/People/Natural_Language_Processing">People</a></li></ul></div><style type="text/css">ul.nav{flex-direction:row}@media(max-width: 567px){ul.nav{flex-direction:column}.TabbedNavigation li{max-width:100%}.TabbedNavigation li.active{background-color:var(--background-grey, #dddde2)}.TabbedNavigation li.active:before,.TabbedNavigation li.active:after{display:none}}</style></div></div></div><div class="container"><div class="row"><div class="col-xs-12"><div class="u-displayFlex"><div class="u-flexGrow1"><div class="works"><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_73302578" data-work_id="73302578" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/73302578/META_GLARE_a_shell_for_CIG_systems">META-GLARE: a shell for CIG systems</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">In the last twenty years, many different approaches to deal with Computer-Interpretable clinical Guidelines (CIGs) have been developed, each one proposing its own representation formalism (mostly based on the Task-Network Model) execution... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_73302578" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">In the last twenty years, many different approaches to deal with Computer-Interpretable clinical Guidelines (CIGs) have been developed, each one proposing its own representation formalism (mostly based on the Task-Network Model) execution engine. We propose META-GLARE a shell for easily defining new CIG systems. Using META-GLARE, CIG system designers can easily define their own systems (basically by defining their representation language), with a minimal programming effort. META-GLARE is thus a flexible and powerful vehicle for research about CIGs, since it supports easy and fast prototyping of new CIG systems.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/73302578" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="496f8f3de6717793c4e23055f2b2f4d8" rel="nofollow" data-download="{"attachment_id":81875097,"asset_id":73302578,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/81875097/download_file?st=MTczOTgzODMzOCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="33352421" href="https://laborange.academia.edu/StefaniaRubrichi">Stefania Rubrichi</a><script data-card-contents-for-user="33352421" type="text/json">{"id":33352421,"first_name":"Stefania","last_name":"Rubrichi","domain_name":"laborange","page_name":"StefaniaRubrichi","display_name":"Stefania Rubrichi","profile_url":"https://laborange.academia.edu/StefaniaRubrichi?f_ri=1432","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_73302578 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="73302578"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 73302578, container: ".js-paper-rank-work_73302578", }); 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$(".js-view-count[data-work-id=73302578]").text(description); $(".js-view-count-work_73302578").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_73302578").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="73302578"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">10</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="422" rel="nofollow" href="https://www.academia.edu/Documents/in/Computer_Science">Computer Science</a>, <script data-card-contents-for-ri="422" type="text/json">{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="451" rel="nofollow" href="https://www.academia.edu/Documents/in/Programming_Languages">Programming Languages</a>, <script data-card-contents-for-ri="451" type="text/json">{"id":451,"name":"Programming Languages","url":"https://www.academia.edu/Documents/in/Programming_Languages?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="1432" rel="nofollow" href="https://www.academia.edu/Documents/in/Natural_Language_Processing">Natural Language Processing</a>, <script data-card-contents-for-ri="1432" type="text/json">{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="26327" rel="nofollow" href="https://www.academia.edu/Documents/in/Medicine">Medicine</a><script data-card-contents-for-ri="26327" type="text/json">{"id":26327,"name":"Medicine","url":"https://www.academia.edu/Documents/in/Medicine?f_ri=1432","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=73302578]'), work: {"id":73302578,"title":"META-GLARE: a shell for CIG systems","created_at":"2022-03-08T02:05:34.129-08:00","url":"https://www.academia.edu/73302578/META_GLARE_a_shell_for_CIG_systems?f_ri=1432","dom_id":"work_73302578","summary":"In the last twenty years, many different approaches to deal with Computer-Interpretable clinical Guidelines (CIGs) have been developed, each one proposing its own representation formalism (mostly based on the Task-Network Model) execution engine. We propose META-GLARE a shell for easily defining new CIG systems. Using META-GLARE, CIG system designers can easily define their own systems (basically by defining their representation language), with a minimal programming effort. META-GLARE is thus a flexible and powerful vehicle for research about CIGs, since it supports easy and fast prototyping of new CIG systems.","downloadable_attachments":[{"id":81875097,"asset_id":73302578,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":33352421,"first_name":"Stefania","last_name":"Rubrichi","domain_name":"laborange","page_name":"StefaniaRubrichi","display_name":"Stefania Rubrichi","profile_url":"https://laborange.academia.edu/StefaniaRubrichi?f_ri=1432","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=1432","nofollow":true},{"id":451,"name":"Programming Languages","url":"https://www.academia.edu/Documents/in/Programming_Languages?f_ri=1432","nofollow":true},{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true},{"id":26327,"name":"Medicine","url":"https://www.academia.edu/Documents/in/Medicine?f_ri=1432","nofollow":true},{"id":45213,"name":"Italy","url":"https://www.academia.edu/Documents/in/Italy?f_ri=1432"},{"id":53293,"name":"Software","url":"https://www.academia.edu/Documents/in/Software?f_ri=1432"},{"id":57939,"name":"Software Design","url":"https://www.academia.edu/Documents/in/Software_Design?f_ri=1432"},{"id":59587,"name":"Library and Information Studies","url":"https://www.academia.edu/Documents/in/Library_and_Information_Studies?f_ri=1432"},{"id":255453,"name":"Information Storage and Retrieval","url":"https://www.academia.edu/Documents/in/Information_Storage_and_Retrieval?f_ri=1432"},{"id":410370,"name":"Public health systems and services research","url":"https://www.academia.edu/Documents/in/Public_health_systems_and_services_research-1?f_ri=1432"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_16692940" data-work_id="16692940" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/16692940/A_survey_of_location_inference_techniques_on_Twitter">A survey of location inference techniques on Twitter</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The increasing popularity of the social networking service, Twitter, has made it more involved in day-to-day communications, strengthening social relationships and information dissemination. Conversations on Twitter are now being explored... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_16692940" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The increasing popularity of the social networking service, Twitter, has made it more involved in day-to-day communications, strengthening social relationships and information dissemination. Conversations on Twitter are now being explored as indicators within early warning systems to alert of imminent natural disasters such earthquakes and aid prompt emergency responses to crime. Producers are privileged to have limitless access to market perception from consumer comments on social media and microblogs. Targeted advertising can be made more effective based on user profile information such as demography, interests and location. While these applications have proven beneficial, the ability to effectively infer the location of Twitter users has even more immense value. However, accurately identifying where a message originated from or author's location remains a challenge thus essentially driving research in that regard. In this paper, we survey a range of techniques applied to infer the location of Twitter users from inception to state-of-the-art. We find significant improvements over time in the granularity levels and better accuracy with results driven by refinements to algorithms and inclusion of more spatial features.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/16692940" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="cc7aedd83bee8f0d8ed08f8538ae8340" rel="nofollow" data-download="{"attachment_id":39123211,"asset_id":16692940,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/39123211/download_file?st=MTczOTgzODMzOCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="36073418" href="https://shu.academia.edu/SeunAjao">Seun Ajao</a><script data-card-contents-for-user="36073418" type="text/json">{"id":36073418,"first_name":"Seun","last_name":"Ajao","domain_name":"shu","page_name":"SeunAjao","display_name":"Seun Ajao","profile_url":"https://shu.academia.edu/SeunAjao?f_ri=1432","photo":"https://0.academia-photos.com/36073418/12433251/13837756/s65_seun.ajao.jpg_oh_1340a56c4a810443a1c10493adb8bd77_oe_5778f12a"}</script></span></span></li><li class="js-paper-rank-work_16692940 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="16692940"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 16692940, container: ".js-paper-rank-work_16692940", }); });</script></li><li class="js-percentile-work_16692940 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 16692940; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_16692940"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_16692940 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="16692940"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 16692940; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=16692940]").text(description); $(".js-view-count-work_16692940").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_16692940").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="16692940"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">8</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="464" rel="nofollow" href="https://www.academia.edu/Documents/in/Information_Retrieval">Information Retrieval</a>, <script data-card-contents-for-ri="464" type="text/json">{"id":464,"name":"Information Retrieval","url":"https://www.academia.edu/Documents/in/Information_Retrieval?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="1432" rel="nofollow" href="https://www.academia.edu/Documents/in/Natural_Language_Processing">Natural Language Processing</a>, <script data-card-contents-for-ri="1432" type="text/json">{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="9246" rel="nofollow" href="https://www.academia.edu/Documents/in/Social_Media">Social Media</a>, <script data-card-contents-for-ri="9246" type="text/json">{"id":9246,"name":"Social Media","url":"https://www.academia.edu/Documents/in/Social_Media?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="11128" rel="nofollow" href="https://www.academia.edu/Documents/in/Information_Extraction">Information Extraction</a><script data-card-contents-for-ri="11128" type="text/json">{"id":11128,"name":"Information Extraction","url":"https://www.academia.edu/Documents/in/Information_Extraction?f_ri=1432","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=16692940]'), work: {"id":16692940,"title":"A survey of location inference techniques on Twitter","created_at":"2015-10-12T05:54:02.269-07:00","url":"https://www.academia.edu/16692940/A_survey_of_location_inference_techniques_on_Twitter?f_ri=1432","dom_id":"work_16692940","summary":"The increasing popularity of the social networking service, Twitter, has made it more involved in day-to-day communications, strengthening social relationships and information dissemination. Conversations on Twitter are now being explored as indicators within early warning systems to alert of imminent natural disasters such earthquakes and aid prompt emergency responses to crime. Producers are privileged to have limitless access to market perception from consumer comments on social media and microblogs. Targeted advertising can be made more effective based on user profile information such as demography, interests and location. While these applications have proven beneficial, the ability to effectively infer the location of Twitter users has even more immense value. However, accurately identifying where a message originated from or author's location remains a challenge thus essentially driving research in that regard. In this paper, we survey a range of techniques applied to infer the location of Twitter users from inception to state-of-the-art. We find significant improvements over time in the granularity levels and better accuracy with results driven by refinements to algorithms and inclusion of more spatial features.","downloadable_attachments":[{"id":39123211,"asset_id":16692940,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":36073418,"first_name":"Seun","last_name":"Ajao","domain_name":"shu","page_name":"SeunAjao","display_name":"Seun Ajao","profile_url":"https://shu.academia.edu/SeunAjao?f_ri=1432","photo":"https://0.academia-photos.com/36073418/12433251/13837756/s65_seun.ajao.jpg_oh_1340a56c4a810443a1c10493adb8bd77_oe_5778f12a"}],"research_interests":[{"id":464,"name":"Information Retrieval","url":"https://www.academia.edu/Documents/in/Information_Retrieval?f_ri=1432","nofollow":true},{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true},{"id":9246,"name":"Social Media","url":"https://www.academia.edu/Documents/in/Social_Media?f_ri=1432","nofollow":true},{"id":11128,"name":"Information Extraction","url":"https://www.academia.edu/Documents/in/Information_Extraction?f_ri=1432","nofollow":true},{"id":12756,"name":"Cyberbullying","url":"https://www.academia.edu/Documents/in/Cyberbullying?f_ri=1432"},{"id":49341,"name":"Event Detection","url":"https://www.academia.edu/Documents/in/Event_Detection?f_ri=1432"},{"id":219580,"name":"Gazetteers","url":"https://www.academia.edu/Documents/in/Gazetteers?f_ri=1432"},{"id":875836,"name":"Language Models","url":"https://www.academia.edu/Documents/in/Language_Models?f_ri=1432"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_50162705" data-work_id="50162705" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/50162705/PharmKE_Knowledge_Extraction_Platform_for_Pharmaceutical_Texts_using_Transfer_Learning">PharmKE: Knowledge Extraction Platform for Pharmaceutical Texts using Transfer Learning</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The challenge of recognizing named entities in a given text has been a very dynamic field in recent years. This is due to the advances in neural network architectures, increase of computing power and the availability of diverse labeled... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_50162705" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The challenge of recognizing named entities in a given text has been a very dynamic field in recent years. This is due to the advances in neural network architectures, increase of computing power and the availability of diverse labeled datasets, which deliver pre-trained, highly accurate models. These tasks are generally focused on tagging common entities, but domain-specific use-cases require tagging custom entities which are not part of the pre-trained models. This can be solved by either fine-tuning the pre-trained models, or by training custom models. The main challenge lies in obtaining reliable labeled training and test datasets, and manual labeling would be a highly tedious task. <br />In this paper we present PharmKE, a text analysis platform focused on the pharmaceutical domain, which applies deep learning through several stages for thorough semantic analysis of pharmaceutical articles. It performs text classification using state-of-the-art transfer learning models, and thoroughly integrates the results obtained through a proposed methodology. The methodology is used to create accurately labeled training and test datasets, which are then used to train models for custom entity labeling tasks, centered on the pharmaceutical domain. The obtained results are compared to the fine-tuned BERT and BioBERT models trained on the same dataset. Additionally, the PharmKE platform integrates the results obtained from named entity recognition tasks to resolve co-references of entities and analyze the semantic relations in every sentence, thus setting up a baseline for additional text analysis tasks, such as question answering and fact extraction. The recognized entities are also used to expand the knowledge graph generated by DBpedia Spotlight for a given pharmaceutical text.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/50162705" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="a414f03d8950a8fcab8a5fa863f44298" rel="nofollow" data-download="{"attachment_id":68252365,"asset_id":50162705,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/68252365/download_file?st=MTczOTgzODMzOCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="36456377" href="https://ukim.academia.edu/MilosJovanovik">Milos Jovanovik</a><script data-card-contents-for-user="36456377" type="text/json">{"id":36456377,"first_name":"Milos","last_name":"Jovanovik","domain_name":"ukim","page_name":"MilosJovanovik","display_name":"Milos Jovanovik","profile_url":"https://ukim.academia.edu/MilosJovanovik?f_ri=1432","photo":"https://0.academia-photos.com/36456377/10957025/31201519/s65_milos.jovanovik.png"}</script></span></span></li><li class="js-paper-rank-work_50162705 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="50162705"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 50162705, container: ".js-paper-rank-work_50162705", }); });</script></li><li class="js-percentile-work_50162705 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 50162705; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_50162705"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_50162705 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="50162705"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 50162705; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=50162705]").text(description); $(".js-view-count-work_50162705").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_50162705").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="50162705"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">5</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="1432" rel="nofollow" href="https://www.academia.edu/Documents/in/Natural_Language_Processing">Natural Language Processing</a>, <script data-card-contents-for-ri="1432" type="text/json">{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="29205" rel="nofollow" href="https://www.academia.edu/Documents/in/Named_Entity_Recognition">Named Entity Recognition</a>, <script data-card-contents-for-ri="29205" type="text/json">{"id":29205,"name":"Named Entity Recognition","url":"https://www.academia.edu/Documents/in/Named_Entity_Recognition?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="62945" rel="nofollow" href="https://www.academia.edu/Documents/in/Drugs">Drugs</a>, <script data-card-contents-for-ri="62945" type="text/json">{"id":62945,"name":"Drugs","url":"https://www.academia.edu/Documents/in/Drugs?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="264064" rel="nofollow" href="https://www.academia.edu/Documents/in/Knowledge_Extraction">Knowledge Extraction</a><script data-card-contents-for-ri="264064" type="text/json">{"id":264064,"name":"Knowledge Extraction","url":"https://www.academia.edu/Documents/in/Knowledge_Extraction?f_ri=1432","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=50162705]'), work: {"id":50162705,"title":"PharmKE: Knowledge Extraction Platform for Pharmaceutical Texts using Transfer Learning","created_at":"2021-07-22T10:28:31.708-07:00","url":"https://www.academia.edu/50162705/PharmKE_Knowledge_Extraction_Platform_for_Pharmaceutical_Texts_using_Transfer_Learning?f_ri=1432","dom_id":"work_50162705","summary":"The challenge of recognizing named entities in a given text has been a very dynamic field in recent years. This is due to the advances in neural network architectures, increase of computing power and the availability of diverse labeled datasets, which deliver pre-trained, highly accurate models. These tasks are generally focused on tagging common entities, but domain-specific use-cases require tagging custom entities which are not part of the pre-trained models. This can be solved by either fine-tuning the pre-trained models, or by training custom models. The main challenge lies in obtaining reliable labeled training and test datasets, and manual labeling would be a highly tedious task. \nIn this paper we present PharmKE, a text analysis platform focused on the pharmaceutical domain, which applies deep learning through several stages for thorough semantic analysis of pharmaceutical articles. It performs text classification using state-of-the-art transfer learning models, and thoroughly integrates the results obtained through a proposed methodology. The methodology is used to create accurately labeled training and test datasets, which are then used to train models for custom entity labeling tasks, centered on the pharmaceutical domain. The obtained results are compared to the fine-tuned BERT and BioBERT models trained on the same dataset. Additionally, the PharmKE platform integrates the results obtained from named entity recognition tasks to resolve co-references of entities and analyze the semantic relations in every sentence, thus setting up a baseline for additional text analysis tasks, such as question answering and fact extraction. The recognized entities are also used to expand the knowledge graph generated by DBpedia Spotlight for a given pharmaceutical text.","downloadable_attachments":[{"id":68252365,"asset_id":50162705,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":36456377,"first_name":"Milos","last_name":"Jovanovik","domain_name":"ukim","page_name":"MilosJovanovik","display_name":"Milos Jovanovik","profile_url":"https://ukim.academia.edu/MilosJovanovik?f_ri=1432","photo":"https://0.academia-photos.com/36456377/10957025/31201519/s65_milos.jovanovik.png"}],"research_interests":[{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true},{"id":29205,"name":"Named Entity Recognition","url":"https://www.academia.edu/Documents/in/Named_Entity_Recognition?f_ri=1432","nofollow":true},{"id":62945,"name":"Drugs","url":"https://www.academia.edu/Documents/in/Drugs?f_ri=1432","nofollow":true},{"id":264064,"name":"Knowledge Extraction","url":"https://www.academia.edu/Documents/in/Knowledge_Extraction?f_ri=1432","nofollow":true},{"id":2886824,"name":"Knowledge graphs","url":"https://www.academia.edu/Documents/in/Knowledge_graphs?f_ri=1432"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_11394590" data-work_id="11394590" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/11394590/The_Visualization_of_Change_in_Word_Meaning_over_Time_using_Temporal_Word_Embeddings">The Visualization of Change in Word Meaning over Time using Temporal Word Embeddings</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">We describe a visualization tool that can be used to view the change in meaning of words over time. The tool makes use of existing (static) word embedding datasets together with a timestamped n-gram cor- pus to create temporal word... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_11394590" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">We describe a visualization tool that can be used to view the change in meaning of words over time. The tool makes use of existing (static) word embedding datasets together with a timestamped n-gram cor- pus to create temporal word embeddings.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/11394590" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="62630d00658ab71fbbd7e1ad93f1d94d" rel="nofollow" data-download="{"attachment_id":36952922,"asset_id":11394590,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/36952922/download_file?st=MTczOTgzODMzOCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="1037271" href="https://independent.academia.edu/ChiraagLala">Chiraag Lala</a><script data-card-contents-for-user="1037271" type="text/json">{"id":1037271,"first_name":"Chiraag","last_name":"Lala","domain_name":"independent","page_name":"ChiraagLala","display_name":"Chiraag Lala","profile_url":"https://independent.academia.edu/ChiraagLala?f_ri=1432","photo":"https://0.academia-photos.com/1037271/668769/8304843/s65_chiraag.lala.jpg"}</script></span></span></li><li class="js-paper-rank-work_11394590 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="11394590"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 11394590, container: ".js-paper-rank-work_11394590", }); });</script></li><li class="js-percentile-work_11394590 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 11394590; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_11394590"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_11394590 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="11394590"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 11394590; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=11394590]").text(description); $(".js-view-count-work_11394590").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_11394590").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="11394590"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">3</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="778" rel="nofollow" href="https://www.academia.edu/Documents/in/Diachronic_Linguistics_Or_Historical_Linguistics_">Diachronic Linguistics (Or Historical Linguistics)</a>, <script data-card-contents-for-ri="778" type="text/json">{"id":778,"name":"Diachronic Linguistics (Or Historical Linguistics)","url":"https://www.academia.edu/Documents/in/Diachronic_Linguistics_Or_Historical_Linguistics_?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="1432" rel="nofollow" href="https://www.academia.edu/Documents/in/Natural_Language_Processing">Natural Language Processing</a>, <script data-card-contents-for-ri="1432" type="text/json">{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="3268" rel="nofollow" href="https://www.academia.edu/Documents/in/Computational_Linguistics">Computational Linguistics</a><script data-card-contents-for-ri="3268" type="text/json">{"id":3268,"name":"Computational Linguistics","url":"https://www.academia.edu/Documents/in/Computational_Linguistics?f_ri=1432","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=11394590]'), work: {"id":11394590,"title":"The Visualization of Change in Word Meaning over Time using Temporal Word Embeddings","created_at":"2015-03-12T10:23:14.476-07:00","url":"https://www.academia.edu/11394590/The_Visualization_of_Change_in_Word_Meaning_over_Time_using_Temporal_Word_Embeddings?f_ri=1432","dom_id":"work_11394590","summary":"We describe a visualization tool that can be used to view the change in meaning of words over time. The tool makes use of existing (static) word embedding datasets together with a timestamped n-gram cor- pus to create temporal word embeddings.","downloadable_attachments":[{"id":36952922,"asset_id":11394590,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":1037271,"first_name":"Chiraag","last_name":"Lala","domain_name":"independent","page_name":"ChiraagLala","display_name":"Chiraag Lala","profile_url":"https://independent.academia.edu/ChiraagLala?f_ri=1432","photo":"https://0.academia-photos.com/1037271/668769/8304843/s65_chiraag.lala.jpg"}],"research_interests":[{"id":778,"name":"Diachronic Linguistics (Or Historical Linguistics)","url":"https://www.academia.edu/Documents/in/Diachronic_Linguistics_Or_Historical_Linguistics_?f_ri=1432","nofollow":true},{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true},{"id":3268,"name":"Computational Linguistics","url":"https://www.academia.edu/Documents/in/Computational_Linguistics?f_ri=1432","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_13255810" data-work_id="13255810" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/13255810/THE_INTERSECTION_BETWEEN_VOCAL_MUSIC_AND_LANGUAGE_ARTS_INSTRUCTION_A_REVIEW_OF_THE_LITERATURE">THE INTERSECTION BETWEEN VOCAL MUSIC AND LANGUAGE ARTS INSTRUCTION: A REVIEW OF THE LITERATURE</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">This paper discusses the interaction of vocal music skill development with phonemic awareness (the ability to hear and manipulate phonemes, the smallest units of language) and fluency training in kindergarten and grade one language arts... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_13255810" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">This paper discusses the interaction of vocal music skill development with phonemic awareness (the ability<br />to hear and manipulate phonemes, the smallest units of language) and fluency training in kindergarten and<br />grade one language arts instruction. The phonemic awareness and fluency activities provided in language<br />arts curricula are often music related: songs, chants and rhymes. Research is discussed regarding auditory<br />processing related to vocal production; developmentally appropriate singing, prosody (the intentional<br />grouping of words into phrases), and music perception as it relates to reading achievement. The<br />pedagogical strategies necessary for developmentally appropriate modeling in both music and language arts<br />instruction are discussed.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/13255810" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="e7197a14865ad00e0c3377c8f194a92b" rel="nofollow" data-download="{"attachment_id":37996279,"asset_id":13255810,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/37996279/download_file?st=MTczOTgzODMzOCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="30976483" href="https://fullerton.academia.edu/PatriciaOHerron">Patricia O'Herron</a><script data-card-contents-for-user="30976483" type="text/json">{"id":30976483,"first_name":"Patricia","last_name":"O'Herron","domain_name":"fullerton","page_name":"PatriciaOHerron","display_name":"Patricia O'Herron","profile_url":"https://fullerton.academia.edu/PatriciaOHerron?f_ri=1432","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_13255810 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="13255810"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 13255810, container: ".js-paper-rank-work_13255810", }); 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The phonemic awareness and fluency activities provided in language\narts curricula are often music related: songs, chants and rhymes. Research is discussed regarding auditory\nprocessing related to vocal production; developmentally appropriate singing, prosody (the intentional\ngrouping of words into phrases), and music perception as it relates to reading achievement. The\npedagogical strategies necessary for developmentally appropriate modeling in both music and language arts\ninstruction are discussed.","downloadable_attachments":[{"id":37996279,"asset_id":13255810,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":30976483,"first_name":"Patricia","last_name":"O'Herron","domain_name":"fullerton","page_name":"PatriciaOHerron","display_name":"Patricia O'Herron","profile_url":"https://fullerton.academia.edu/PatriciaOHerron?f_ri=1432","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":683,"name":"Music Education","url":"https://www.academia.edu/Documents/in/Music_Education?f_ri=1432","nofollow":true},{"id":1200,"name":"Languages and Linguistics","url":"https://www.academia.edu/Documents/in/Languages_and_Linguistics?f_ri=1432","nofollow":true},{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true},{"id":1601,"name":"Teacher Education","url":"https://www.academia.edu/Documents/in/Teacher_Education?f_ri=1432","nofollow":true},{"id":17769,"name":"Professional Development","url":"https://www.academia.edu/Documents/in/Professional_Development?f_ri=1432"},{"id":36556,"name":"Elementary Education","url":"https://www.academia.edu/Documents/in/Elementary_Education?f_ri=1432"},{"id":41144,"name":"Prosody","url":"https://www.academia.edu/Documents/in/Prosody?f_ri=1432"},{"id":147243,"name":"teaching Strategies","url":"https://www.academia.edu/Documents/in/teaching_Strategies?f_ri=1432"},{"id":170131,"name":"Vocal Music","url":"https://www.academia.edu/Documents/in/Vocal_Music?f_ri=1432"},{"id":173410,"name":"Phonemic Awareness","url":"https://www.academia.edu/Documents/in/Phonemic_Awareness?f_ri=1432"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_34736928" data-work_id="34736928" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/34736928/D%C4%93_r%C4%93rum_nat%C5%ABra_pdf">Dē rērum natūra.pdf</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/34736928" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="ea8e8307e9fa7416df31f1101d1ccfc6" rel="nofollow" data-download="{"attachment_id":54594658,"asset_id":34736928,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/54594658/download_file?st=MTczOTgzODMzOCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="68903019" href="https://independent.academia.edu/Jos%C3%A9Durigon">José Durigon</a><script data-card-contents-for-user="68903019" type="text/json">{"id":68903019,"first_name":"José","last_name":"Durigon","domain_name":"independent","page_name":"JoséDurigon","display_name":"José Durigon","profile_url":"https://independent.academia.edu/Jos%C3%A9Durigon?f_ri=1432","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_34736928 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="34736928"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 34736928, container: ".js-paper-rank-work_34736928", }); 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The Conference <br />looks for significant contributions to all major fields of the Artificial Intelligence, Soft Computing in theoretical and practical aspects. The aim of the Conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and <br />share cutting-edge development in the field. Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/45631677" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="9fd7273008a16e18a7106b5e2043af26" rel="nofollow" data-download="{"attachment_id":74727553,"asset_id":45631677,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/74727553/download_file?st=MTczOTgzODMzOCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="19082339" rel="nofollow" href="https://independent.academia.edu/IjaiaJournal">International Journal of Artificial Intelligence (IJAIA)</a><script data-card-contents-for-user="19082339" type="text/json">{"id":19082339,"first_name":"International Journal of Artificial Intelligence","last_name":"(IJAIA)","domain_name":"independent","page_name":"IjaiaJournal","display_name":"International Journal of Artificial Intelligence (IJAIA)","profile_url":"https://independent.academia.edu/IjaiaJournal?f_ri=1432","photo":"https://0.academia-photos.com/19082339/5301872/160061290/s65_international_journal_of_artificial_intelligence_applications._ijaia_.jpg"}</script></span></span></li><li class="js-paper-rank-work_45631677 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="45631677"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 45631677, container: ".js-paper-rank-work_45631677", }); 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The Conference \nlooks for significant contributions to all major fields of the Artificial Intelligence, Soft Computing in theoretical and practical aspects. The aim of the Conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and \nshare cutting-edge development in the field. Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to.\n","downloadable_attachments":[{"id":74727553,"asset_id":45631677,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":19082339,"first_name":"International Journal of Artificial Intelligence","last_name":"(IJAIA)","domain_name":"independent","page_name":"IjaiaJournal","display_name":"International Journal of Artificial Intelligence 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u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/3878477/INTRODUCTION_TO_ARTIFICIAL_NEURAL_NETWORK_AND_MACHINE_LEARNING">INTRODUCTION TO ARTIFICIAL NEURAL NETWORK AND MACHINE LEARNING</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/3878477" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="b2f2243638090641e2f6a04cf5ae235e" rel="nofollow" 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u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">El objetivo que se pretende alcanzar consiste en desarrollar una habilidad metacognitiva: La utilización de los recursos lingüísticos y comunicativos de forma consciente y discrecional aplicados al habla dirigida a los niños que... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_22861593" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">El objetivo que se pretende alcanzar consiste en desarrollar una habilidad  metacognitiva: La utilización de los recursos  lingüísticos y comunicativos de forma consciente y discrecional aplicados al habla dirigida a los niños que presenten retrasos de lenguaje. Este uso permitirá valorar los efectos conseguidos en la tarea de desarrollar formas más elaboradas, disminuir los errores, evitar la falta de respuesta verbal o aumentar la fluidez dentro de un marco comunicativo.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/22861593" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="ed020f5d761bc6b73eb9d59fb8c344d7" rel="nofollow" data-download="{"attachment_id":43401934,"asset_id":22861593,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/43401934/download_file?st=MTczOTgzODMzOCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="44519159" href="https://independent.academia.edu/MagalistolovitskyColb">Magali stolovitsky Colb</a><script data-card-contents-for-user="44519159" type="text/json">{"id":44519159,"first_name":"Magali","last_name":"stolovitsky Colb","domain_name":"independent","page_name":"MagalistolovitskyColb","display_name":"Magali stolovitsky Colb","profile_url":"https://independent.academia.edu/MagalistolovitskyColb?f_ri=1432","photo":"https://0.academia-photos.com/44519159/12128900/13509817/s65_magali.stolovitsky_colb.jpg"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-22861593">+1</span><div class="hidden js-additional-users-22861593"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://independent.academia.edu/MonicaRosseau">Monica Rousseau</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-22861593'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-22861593').html(); 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","downloadable_attachments":[{"id":43401934,"asset_id":22861593,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":44519159,"first_name":"Magali","last_name":"stolovitsky Colb","domain_name":"independent","page_name":"MagalistolovitskyColb","display_name":"Magali stolovitsky Colb","profile_url":"https://independent.academia.edu/MagalistolovitskyColb?f_ri=1432","photo":"https://0.academia-photos.com/44519159/12128900/13509817/s65_magali.stolovitsky_colb.jpg"},{"id":44566481,"first_name":"Monica","last_name":"Rousseau","domain_name":"independent","page_name":"MonicaRosseau","display_name":"Monica Rousseau","profile_url":"https://independent.academia.edu/MonicaRosseau?f_ri=1432","photo":"https://0.academia-photos.com/44566481/11852848/36078954/s65_monica.rousseau.jpg"}],"research_interests":[{"id":1200,"name":"Languages and Linguistics","url":"https://www.academia.edu/Documents/in/Languages_and_Linguistics?f_ri=1432","nofollow":true},{"id":1432,"name":"Natural 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class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/16681935/%D0%94%D0%B2%D0%B5_%D0%B1%D0%B0%D0%BB%D0%BA%D0%B0%D0%BD%D1%81%D0%BA%D0%B8_%D0%BA%D0%BE%D0%BD%D1%84%D0%B5%D1%80%D0%B5%D0%BD%D1%86%D0%B8%D0%B8_%D0%BF%D0%BE%D1%81%D0%B2%D0%B5%D1%82%D0%B5%D0%BD%D0%B8_%D0%BD%D0%B0_%D0%BA%D0%BE%D0%BC%D0%BF%D1%8E%D1%82%D1%8A%D1%80%D0%BD%D0%B0%D1%82%D0%B0_%D0%BE%D0%B1%D1%80%D0%B0%D0%B1%D0%BE%D1%82%D0%BA%D0%B0_%D0%BD%D0%B0_%D0%B5%D0%B7%D0%B8%D0%BA%D0%B0">Две балкански конференции, посветени на компютърната обработка на езика</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest">Хрониката представя две конференци, посветени на компютърната обработка на езика – Nooj 2010 и FASBL 2010.</div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm 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","downloadable_attachments":[{"id":55693891,"asset_id":16681935,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":9501159,"first_name":"Rositsa","last_name":"Dekova","domain_name":"uni-plovdiv","page_name":"RositsaDekova","display_name":"Rositsa Dekova","profile_url":"https://uni-plovdiv.academia.edu/RositsaDekova?f_ri=1432","photo":"https://0.academia-photos.com/9501159/3013231/3539302/s65_rositsa.dekova.jpg"}],"research_interests":[{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true},{"id":2715,"name":"Slavic Languages","url":"https://www.academia.edu/Documents/in/Slavic_Languages?f_ri=1432","nofollow":true},{"id":15674,"name":"Linguistics","url":"https://www.academia.edu/Documents/in/Linguistics?f_ri=1432","nofollow":true},{"id":249519,"name":"Balkan languages","url":"https://www.academia.edu/Documents/in/Balkan_languages?f_ri=1432","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_53667751" data-work_id="53667751" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" rel="nofollow" href="https://www.academia.edu/53667751/CALL_FOR_PAPERS_9_th_International_Conference_of_Artificial_Intelligence_and_Fuzzy_Logic_AI_and_FL_2021_">CALL FOR PAPERS - 9 th International Conference of Artificial Intelligence and Fuzzy Logic (AI & FL 2021)</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">9 th International Conference of Artificial Intelligence and Fuzzy Logic (AI & FL 2021) provides a forum for researchers who address this issue and to present their work in a peerreviewed forum. Authors are solicited to contribute to the... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_53667751" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">9<br />th International Conference of Artificial Intelligence and Fuzzy Logic (AI & FL 2021)<br />provides a forum for researchers who address this issue and to present their work in a peerreviewed forum. Authors are solicited to contribute to the conference by submitting articles that <br />illustrate research results, projects, surveying works and industrial experiences that describe <br />significant advances in the following areas, but are not limited to these topics only.<br />Authors are solicited to contribute to this conference by submitting articles that illustrate <br />research results, projects, surveying works and industrial experiences that describe significant <br />advances in the areas of Artificial Intelligence & applications</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/53667751" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="b516ab237d8e661815a768c9c2bda8fa" rel="nofollow" data-download="{"attachment_id":74355440,"asset_id":53667751,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/74355440/download_file?st=MTczOTgzODMzOCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="74164909" rel="nofollow" href="https://annauniv.academia.edu/ijitcsjournal">International Journal of Information Technology Convergence and services (IJITCS)</a><script data-card-contents-for-user="74164909" type="text/json">{"id":74164909,"first_name":"International Journal of Information Technology Convergence and services","last_name":"(IJITCS)","domain_name":"annauniv","page_name":"ijitcsjournal","display_name":"International Journal of Information Technology Convergence and services (IJITCS)","profile_url":"https://annauniv.academia.edu/ijitcsjournal?f_ri=1432","photo":"https://0.academia-photos.com/74164909/18840627/84065623/s65_international_journal_of_information_technology_convergence_and_services._ijitcs_.jpg"}</script></span></span></li><li class="js-paper-rank-work_53667751 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="53667751"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 53667751, container: ".js-paper-rank-work_53667751", }); 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href="https://www.academia.edu/1376374/Natural_Resource_perspectives_Number_51_March_2000_This_series_is_published_by_ODI_an_independent_non_profit_policy_research_institute_with_financial_support_from_the_Department_for_International_Development_formerly_the_Overseas_Development_Administration_Opinions_expressed_do_not_necessarily_reflect_the_views_of_either_ODI_or_DFID_DFID_Department_for_International_Development_Introduction_Putting_poverty_at_the_heart_of_the_tourism_agenda_Lack_of_focus_on_poverty_within_the_tourism_agenda_The_International_Development_Target_of_halving_the_proportion_of_people_living_in_extreme_poverty_by_2015_has_been_widely_adopted_A_number_of_prominent_development_agencies_including_the_UK_s_Department_for_International_Development_DFID_are_developing_sustainable_livelihoods_approaches_in_response_to_these_targets_In_the_tourism_sector_national_governments_and_donors_have_generally_aimed_to_promote_private_sector_investment_macro_economic_growth_and_foreign_exchange_earnings_without_s">Natural Resource perspectives Number 51, March 2000 This series is published by ODI, an independent non-profit policy research institute, with financial support from the Department for International Development (formerly the Overseas Development Administration). Opinions expressed do not necessarily reflect the views of either ODI or DFID. DFID Department for International Development Introduction: Putting poverty at the heart of the tourism agenda Lack of focus on poverty within the tourism agenda The International Development Target of halving the proportion of people living in extreme poverty by 2015 has been widely adopted. A number of prominent development agencies, including the UK’s Department for International Development (DFID), are developing sustainable livelihoods approaches in response to these targets. In the tourism sector, national governments and donors have generally aimed to promote private sector investment, macro-economic growth and foreign exchange earnings, without specifically taking the needs and opportunities of the poor into account in tourism development (i.e. what we term here ‘pro-poor tourism’). Donor-supported tourism master plans focus on creating infrastructure, stimulating private investment and attracting international tourists. Investors are often international companies and local élites, whose profits are generally repatriated abroad or to metropolitan centres. Links with the local economy are often weak, with the possible exception of employment. Since the mid-1980s, interest in ‘green’ tourism, eco-tourism and community tourism has grown rapidly among decisionmakers, practitioners and advocates. All of these focus on the need to ensure that tourism does not erode the environmental and cultural base on which it depends. But these generally do not consider the full range of impacts on the livelihoods of the poor. The current challenge for governments and donors in tourism development is to respond to changes in broader development thinking, by developing strategies to enhance impacts of tourism on the poor. Recent research in India, Indonesia, Namibia, Nepal, the Philippines, Uganda, Zambia and Zimbabwe helps to shed light on the issues involved. Can tourism really be pro-poor? Tourism is a complex industry driven by the private sector, and often by large international companies. Governments have relatively few instruments to influence this sector, particularly in developing countries where fiscal and planning instruments for capturing non-commercial benefits are generally weak. Nevertheless, as a sector for pro-poor economic growth, tourism has several advantages: • The consumer comes to the destination, thereby providing opportunities for selling additional goods and services. • Tourism is an important opportunity to diversify local economies. It can develop in poor and marginal areas with few other export and diversification options. Remote Box 1 Significance of tourism in poor countries In 1997 developing countries received 30.5% of world international tourist arrivals, compared with 24% in 1988 (WTO, 1998). International tourism is significant (over 2% of GDP or 5% of exports) or growing (i.e. by at least 50% in 1990–7) in almost half of the 48 low income countries, and in virtually all the 53 low income and middle income countries. Among the 12 countries that are home to 80% of the world’s poor, tourism is significant or growing in all but one. Problems of definition These statistics only cover some economic aspects of international tourism. Macro-economic data generally only capture arrivals and foreign exchange receipts associated with international tourism. There are two problems with this: • They do not capture domestic tourism, nor do they disaggregate regional tourism, both of which are significant and growing in Asia, Africa and South America, and often important markets for the poor. • Foreign exchange receipts do not accurately reflect the economic contribution of tourism. In addition to ‘core’ services of accommodation and transport, the tourism-related economy also involves food and drinks, supplies to hotels, local transport and attractions, guiding, handicrafts and souvenirs. Tourismrelated services are particularly important for expanding participation by the poor. PRO-POOR TOURISM: PUTTING POVERTY AT THE HEART OF THE TOURISM AGENDA Caroline Ashley, Charlotte Boyd and Harold Goodwin</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Natural Resource perspectives Number 51, March 2000 This series is published by ODI, an independent non-profit policy research institute, with financial support from the Department for International Development (formerly the Overseas... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_1376374" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Natural Resource <br />perspectives <br />Number 51, March 2000 <br />This series is published by ODI, an independent non-profit policy research institute, with financial <br />support from the Department for International Development (formerly the Overseas Development <br />Administration). Opinions expressed do not necessarily reflect the views of either ODI or DFID. DFID Department for <br />International <br />Development <br />Introduction: Putting poverty at the heart of <br />the tourism agenda <br />Lack of focus on poverty within the tourism agenda <br />The International Development Target of halving the <br />proportion of people living in extreme poverty by 2015 has <br />been widely adopted. A number of prominent development <br />agencies, including the UK’s Department for International <br />Development (DFID), are developing sustainable livelihoods <br />approaches in response to these targets. <br />In the tourism sector, national governments and donors <br />have generally aimed to promote private sector investment, <br />macro-economic growth and foreign exchange earnings, <br />without specifically taking the needs and opportunities of <br />the poor into account in tourism development (i.e. what we <br />term here ‘pro-poor tourism’). Donor-supported tourism <br />master plans focus on creating infrastructure, stimulating <br />private investment and attracting international tourists. <br />Investors are often international companies and local élites, <br />whose profits are generally repatriated abroad or to <br />metropolitan centres. Links with the local economy are often <br />weak, with the possible exception of employment. <br />Since the mid-1980s, interest in ‘green’ tourism, eco-tourism <br />and community tourism has grown rapidly among decisionmakers, <br />practitioners and advocates. All of these focus on <br />the need to ensure that tourism does not erode the <br />environmental and cultural base on which it depends. But <br />these generally do not consider the full range of impacts on <br />the livelihoods of the poor. <br />The current challenge for governments and donors in <br />tourism development is to respond to changes in broader <br />development thinking, by developing strategies to enhance <br />impacts of tourism on the poor. Recent research in India, <br />Indonesia, Namibia, Nepal, the Philippines, Uganda, Zambia <br />and Zimbabwe helps to shed light on the issues involved. <br />Can tourism really be pro-poor? <br />Tourism is a complex industry driven by the private sector, <br />and often by large international companies. Governments <br />have relatively few instruments to influence this sector, <br />particularly in developing countries where fiscal and planning <br />instruments for capturing non-commercial benefits are <br />generally weak. <br />Nevertheless, as a sector for pro-poor economic growth, <br />tourism has several advantages: <br />• The consumer comes to the destination, thereby providing <br />opportunities for selling additional goods and services. <br />• Tourism is an important opportunity to diversify local <br />economies. It can develop in poor and marginal areas <br />with few other export and diversification options. Remote <br />Box 1 Significance of tourism in poor countries <br />In 1997 developing countries received 30.5% of world <br />international tourist arrivals, compared with 24% in 1988 (WTO, <br />1998). International tourism is significant (over 2% of GDP or 5% <br />of exports) or growing (i.e. by at least 50% in 1990–7) in almost <br />half of the 48 low income countries, and in virtually all the 53 <br />low income and middle income countries. Among the 12 countries <br />that are home to 80% of the world’s poor, tourism is significant or <br />growing in all but one. <br />Problems of definition <br />These statistics only cover some economic aspects of international <br />tourism. Macro-economic data generally only capture arrivals and <br />foreign exchange receipts associated with international tourism. <br />There are two problems with this: <br />• They do not capture domestic tourism, nor do they disaggregate <br />regional tourism, both of which are significant and growing in <br />Asia, Africa and South America, and often important markets <br />for the poor. <br />• Foreign exchange receipts do not accurately reflect the <br />economic contribution of tourism. In addition to ‘core’ services <br />of accommodation and transport, the tourism-related economy <br />also involves food and drinks, supplies to hotels, local transport <br />and attractions, guiding, handicrafts and souvenirs. Tourismrelated <br />services are particularly important for expanding <br />participation by the poor. <br />PRO-POOR TOURISM: PUTTING POVERTY AT THE HEART OF THE <br />TOURISM AGENDA <br />Caroline Ashley, Charlotte Boyd and Harold Goodwin</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/1376374" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="16550450e6ac8f662406b4681ff0a648" rel="nofollow" data-download="{"attachment_id":8596389,"asset_id":1376374,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/8596389/download_file?st=MTczOTgzODMzOCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="780585" href="https://cambridge-centralasia.academia.edu/boracayisland">Historically Digitized</a><script data-card-contents-for-user="780585" type="text/json">{"id":780585,"first_name":"Historically","last_name":"Digitized","domain_name":"cambridge-centralasia","page_name":"boracayisland","display_name":"Historically Digitized","profile_url":"https://cambridge-centralasia.academia.edu/boracayisland?f_ri=1432","photo":"https://0.academia-photos.com/780585/265877/314189/s65_historically.digitized.jpg"}</script></span></span></li><li class="js-paper-rank-work_1376374 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="1376374"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 1376374, container: ".js-paper-rank-work_1376374", }); 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$(".js-view-count[data-work-id=1376374]").text(description); $(".js-view-count-work_1376374").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_1376374").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="1376374"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">6</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="47" rel="nofollow" href="https://www.academia.edu/Documents/in/Finance">Finance</a>, <script data-card-contents-for-ri="47" type="text/json">{"id":47,"name":"Finance","url":"https://www.academia.edu/Documents/in/Finance?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="724" rel="nofollow" href="https://www.academia.edu/Documents/in/Economics">Economics</a>, <script data-card-contents-for-ri="724" type="text/json">{"id":724,"name":"Economics","url":"https://www.academia.edu/Documents/in/Economics?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="1432" rel="nofollow" href="https://www.academia.edu/Documents/in/Natural_Language_Processing">Natural Language Processing</a>, <script data-card-contents-for-ri="1432" type="text/json">{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="1483" rel="nofollow" href="https://www.academia.edu/Documents/in/Foreign_Policy_Analysis">Foreign Policy Analysis</a><script data-card-contents-for-ri="1483" type="text/json">{"id":1483,"name":"Foreign Policy Analysis","url":"https://www.academia.edu/Documents/in/Foreign_Policy_Analysis?f_ri=1432","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=1376374]'), work: {"id":1376374,"title":"Natural Resource perspectives Number 51, March 2000 This series is published by ODI, an independent non-profit policy research institute, with financial support from the Department for International Development (formerly the Overseas Development Administration). Opinions expressed do not necessarily reflect the views of either ODI or DFID. DFID Department for International Development Introduction: Putting poverty at the heart of the tourism agenda Lack of focus on poverty within the tourism agenda The International Development Target of halving the proportion of people living in extreme poverty by 2015 has been widely adopted. A number of prominent development agencies, including the UK’s Department for International Development (DFID), are developing sustainable livelihoods approaches in response to these targets. In the tourism sector, national governments and donors have generally aimed to promote private sector investment, macro-economic growth and foreign exchange earnings, without specifically taking the needs and opportunities of the poor into account in tourism development (i.e. what we term here ‘pro-poor tourism’). Donor-supported tourism master plans focus on creating infrastructure, stimulating private investment and attracting international tourists. Investors are often international companies and local élites, whose profits are generally repatriated abroad or to metropolitan centres. Links with the local economy are often weak, with the possible exception of employment. Since the mid-1980s, interest in ‘green’ tourism, eco-tourism and community tourism has grown rapidly among decisionmakers, practitioners and advocates. All of these focus on the need to ensure that tourism does not erode the environmental and cultural base on which it depends. But these generally do not consider the full range of impacts on the livelihoods of the poor. The current challenge for governments and donors in tourism development is to respond to changes in broader development thinking, by developing strategies to enhance impacts of tourism on the poor. Recent research in India, Indonesia, Namibia, Nepal, the Philippines, Uganda, Zambia and Zimbabwe helps to shed light on the issues involved. Can tourism really be pro-poor? Tourism is a complex industry driven by the private sector, and often by large international companies. Governments have relatively few instruments to influence this sector, particularly in developing countries where fiscal and planning instruments for capturing non-commercial benefits are generally weak. Nevertheless, as a sector for pro-poor economic growth, tourism has several advantages: • The consumer comes to the destination, thereby providing opportunities for selling additional goods and services. • Tourism is an important opportunity to diversify local economies. It can develop in poor and marginal areas with few other export and diversification options. Remote Box 1 Significance of tourism in poor countries In 1997 developing countries received 30.5% of world international tourist arrivals, compared with 24% in 1988 (WTO, 1998). International tourism is significant (over 2% of GDP or 5% of exports) or growing (i.e. by at least 50% in 1990–7) in almost half of the 48 low income countries, and in virtually all the 53 low income and middle income countries. Among the 12 countries that are home to 80% of the world’s poor, tourism is significant or growing in all but one. Problems of definition These statistics only cover some economic aspects of international tourism. Macro-economic data generally only capture arrivals and foreign exchange receipts associated with international tourism. There are two problems with this: • They do not capture domestic tourism, nor do they disaggregate regional tourism, both of which are significant and growing in Asia, Africa and South America, and often important markets for the poor. • Foreign exchange receipts do not accurately reflect the economic contribution of tourism. In addition to ‘core’ services of accommodation and transport, the tourism-related economy also involves food and drinks, supplies to hotels, local transport and attractions, guiding, handicrafts and souvenirs. Tourismrelated services are particularly important for expanding participation by the poor. PRO-POOR TOURISM: PUTTING POVERTY AT THE HEART OF THE TOURISM AGENDA Caroline Ashley, Charlotte Boyd and Harold Goodwin","created_at":"2012-02-11T17:31:08.149-08:00","url":"https://www.academia.edu/1376374/Natural_Resource_perspectives_Number_51_March_2000_This_series_is_published_by_ODI_an_independent_non_profit_policy_research_institute_with_financial_support_from_the_Department_for_International_Development_formerly_the_Overseas_Development_Administration_Opinions_expressed_do_not_necessarily_reflect_the_views_of_either_ODI_or_DFID_DFID_Department_for_International_Development_Introduction_Putting_poverty_at_the_heart_of_the_tourism_agenda_Lack_of_focus_on_poverty_within_the_tourism_agenda_The_International_Development_Target_of_halving_the_proportion_of_people_living_in_extreme_poverty_by_2015_has_been_widely_adopted_A_number_of_prominent_development_agencies_including_the_UK_s_Department_for_International_Development_DFID_are_developing_sustainable_livelihoods_approaches_in_response_to_these_targets_In_the_tourism_sector_national_governments_and_donors_have_generally_aimed_to_promote_private_sector_investment_macro_economic_growth_and_foreign_exchange_earnings_without_s?f_ri=1432","dom_id":"work_1376374","summary":"Natural Resource\r\nperspectives\r\nNumber 51, March 2000\r\nThis series is published by ODI, an independent non-profit policy research institute, with financial\r\nsupport from the Department for International Development (formerly the Overseas Development\r\nAdministration). Opinions expressed do not necessarily reflect the views of either ODI or DFID. DFID Department for\r\nInternational\r\nDevelopment\r\nIntroduction: Putting poverty at the heart of\r\nthe tourism agenda\r\nLack of focus on poverty within the tourism agenda\r\nThe International Development Target of halving the\r\nproportion of people living in extreme poverty by 2015 has\r\nbeen widely adopted. A number of prominent development\r\nagencies, including the UK’s Department for International\r\nDevelopment (DFID), are developing sustainable livelihoods\r\napproaches in response to these targets.\r\nIn the tourism sector, national governments and donors\r\nhave generally aimed to promote private sector investment,\r\nmacro-economic growth and foreign exchange earnings,\r\nwithout specifically taking the needs and opportunities of\r\nthe poor into account in tourism development (i.e. what we\r\nterm here ‘pro-poor tourism’). Donor-supported tourism\r\nmaster plans focus on creating infrastructure, stimulating\r\nprivate investment and attracting international tourists.\r\nInvestors are often international companies and local élites,\r\nwhose profits are generally repatriated abroad or to\r\nmetropolitan centres. Links with the local economy are often\r\nweak, with the possible exception of employment.\r\nSince the mid-1980s, interest in ‘green’ tourism, eco-tourism\r\nand community tourism has grown rapidly among decisionmakers,\r\npractitioners and advocates. All of these focus on\r\nthe need to ensure that tourism does not erode the\r\nenvironmental and cultural base on which it depends. But\r\nthese generally do not consider the full range of impacts on\r\nthe livelihoods of the poor.\r\nThe current challenge for governments and donors in\r\ntourism development is to respond to changes in broader\r\ndevelopment thinking, by developing strategies to enhance\r\nimpacts of tourism on the poor. Recent research in India,\r\nIndonesia, Namibia, Nepal, the Philippines, Uganda, Zambia\r\nand Zimbabwe helps to shed light on the issues involved.\r\nCan tourism really be pro-poor?\r\nTourism is a complex industry driven by the private sector,\r\nand often by large international companies. Governments\r\nhave relatively few instruments to influence this sector,\r\nparticularly in developing countries where fiscal and planning\r\ninstruments for capturing non-commercial benefits are\r\ngenerally weak.\r\nNevertheless, as a sector for pro-poor economic growth,\r\ntourism has several advantages:\r\n• The consumer comes to the destination, thereby providing\r\nopportunities for selling additional goods and services.\r\n• Tourism is an important opportunity to diversify local\r\neconomies. It can develop in poor and marginal areas\r\nwith few other export and diversification options. Remote\r\nBox 1 Significance of tourism in poor countries\r\nIn 1997 developing countries received 30.5% of world\r\ninternational tourist arrivals, compared with 24% in 1988 (WTO,\r\n1998). International tourism is significant (over 2% of GDP or 5%\r\nof exports) or growing (i.e. by at least 50% in 1990–7) in almost\r\nhalf of the 48 low income countries, and in virtually all the 53\r\nlow income and middle income countries. Among the 12 countries\r\nthat are home to 80% of the world’s poor, tourism is significant or\r\ngrowing in all but one.\r\nProblems of definition\r\nThese statistics only cover some economic aspects of international\r\ntourism. Macro-economic data generally only capture arrivals and\r\nforeign exchange receipts associated with international tourism.\r\nThere are two problems with this:\r\n• They do not capture domestic tourism, nor do they disaggregate\r\nregional tourism, both of which are significant and growing in\r\nAsia, Africa and South America, and often important markets\r\nfor the poor.\r\n• Foreign exchange receipts do not accurately reflect the\r\neconomic contribution of tourism. In addition to ‘core’ services\r\nof accommodation and transport, the tourism-related economy\r\nalso involves food and drinks, supplies to hotels, local transport\r\nand attractions, guiding, handicrafts and souvenirs. Tourismrelated\r\nservices are particularly important for expanding\r\nparticipation by the poor.\r\nPRO-POOR TOURISM: PUTTING POVERTY AT THE HEART OF THE\r\nTOURISM AGENDA\r\nCaroline Ashley, Charlotte Boyd and Harold Goodwin","downloadable_attachments":[{"id":8596389,"asset_id":1376374,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":780585,"first_name":"Historically","last_name":"Digitized","domain_name":"cambridge-centralasia","page_name":"boracayisland","display_name":"Historically Digitized","profile_url":"https://cambridge-centralasia.academia.edu/boracayisland?f_ri=1432","photo":"https://0.academia-photos.com/780585/265877/314189/s65_historically.digitized.jpg"}],"research_interests":[{"id":47,"name":"Finance","url":"https://www.academia.edu/Documents/in/Finance?f_ri=1432","nofollow":true},{"id":724,"name":"Economics","url":"https://www.academia.edu/Documents/in/Economics?f_ri=1432","nofollow":true},{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true},{"id":1483,"name":"Foreign Policy Analysis","url":"https://www.academia.edu/Documents/in/Foreign_Policy_Analysis?f_ri=1432","nofollow":true},{"id":4486,"name":"Political Science","url":"https://www.academia.edu/Documents/in/Political_Science?f_ri=1432"},{"id":18845,"name":"Environmental Sustainability","url":"https://www.academia.edu/Documents/in/Environmental_Sustainability?f_ri=1432"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_35333178" data-work_id="35333178" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/35333178/Indeterminatezza_e_mutamento_linguistico">Indeterminatezza e mutamento linguistico</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">This study aims at providing a possible model for linguistic representation, whose structure is able to describe the effects on sign’s indeterminacy (vagueness, ambiguities) and the dynamic nature of the system. In order to display the... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_35333178" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">This study aims at providing a possible model for linguistic representation, whose structure is able to describe the effects on sign’s indeterminacy (vagueness, ambiguities) and the dynamic nature of the system. In order to display the different ways of conceiving a linguistic system, various versions of the idea of "giving an order to the structures", that build up a theory, is taken into account: the Cartesian theory of generative grammar, the non-Euclidean one of hypertext networks, the complexity in genetic epistemology and in Jean Piaget’s cognitive development theory, and finally the theory of linguistic change by the language biologist Eric Lenneberg.<br /><br />Scopo di questo studio è il tentativo di ipotizzare un modello di rappresentazione linguistica, la cui struttura sia capace di descrivere gli effetti dell'indeterminatezza del segno (vaghezza, ambiguità) e la dinamicità del sistema. Per mostrare differenti possibilità di concepire astrattamente un sistema linguistico, si considerano versioni diverse del concetto di “ordine delle strutture”: il sistema cartesiano nella grammatica generativa, la geometria non euclidea nelle reti ipertestuali, la (teoria della )complessità nella teoria dello sviluppo psicogenetico di Jean Piaget e nella teoria del mutamento del biolinguista Eric Lenneberg.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/35333178" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="27ebab3f861ff80264dffdb30d08f5d2" rel="nofollow" data-download="{"attachment_id":55194424,"asset_id":35333178,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/55194424/download_file?st=MTczOTgzODMzOCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="6749120" href="https://cnr-it.academia.edu/GianCarloFedeli">Gian Carlo Fedeli</a><script data-card-contents-for-user="6749120" type="text/json">{"id":6749120,"first_name":"Gian Carlo","last_name":"Fedeli","domain_name":"cnr-it","page_name":"GianCarloFedeli","display_name":"Gian Carlo Fedeli","profile_url":"https://cnr-it.academia.edu/GianCarloFedeli?f_ri=1432","photo":"https://0.academia-photos.com/6749120/2861053/3341300/s65_gian_carlo.fedeli.jpg"}</script></span></span></li><li class="js-paper-rank-work_35333178 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="35333178"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 35333178, container: ".js-paper-rank-work_35333178", }); 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In order to display the different ways of conceiving a linguistic system, various versions of the idea of \"giving an order to the structures\", that build up a theory, is taken into account: the Cartesian theory of generative grammar, the non-Euclidean one of hypertext networks, the complexity in genetic epistemology and in Jean Piaget’s cognitive development theory, and finally the theory of linguistic change by the language biologist Eric Lenneberg.\n\nScopo di questo studio è il tentativo di ipotizzare un modello di rappresentazione linguistica, la cui struttura sia capace di descrivere gli effetti dell'indeterminatezza del segno (vaghezza, ambiguità) e la dinamicità del sistema. Per mostrare differenti possibilità di concepire astrattamente un sistema linguistico, si considerano versioni diverse del concetto di “ordine delle strutture”: il sistema cartesiano nella grammatica generativa, la geometria non euclidea nelle reti ipertestuali, la (teoria della )complessità nella teoria dello sviluppo psicogenetico di Jean Piaget e nella teoria del mutamento del biolinguista Eric Lenneberg.","downloadable_attachments":[{"id":55194424,"asset_id":35333178,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":6749120,"first_name":"Gian Carlo","last_name":"Fedeli","domain_name":"cnr-it","page_name":"GianCarloFedeli","display_name":"Gian Carlo Fedeli","profile_url":"https://cnr-it.academia.edu/GianCarloFedeli?f_ri=1432","photo":"https://0.academia-photos.com/6749120/2861053/3341300/s65_gian_carlo.fedeli.jpg"}],"research_interests":[{"id":807,"name":"Philosophy Of Language","url":"https://www.academia.edu/Documents/in/Philosophy_Of_Language?f_ri=1432","nofollow":true},{"id":815,"name":"Epistemology","url":"https://www.academia.edu/Documents/in/Epistemology?f_ri=1432","nofollow":true},{"id":1200,"name":"Languages and Linguistics","url":"https://www.academia.edu/Documents/in/Languages_and_Linguistics?f_ri=1432","nofollow":true},{"id":1417,"name":"Language Acquisition","url":"https://www.academia.edu/Documents/in/Language_Acquisition?f_ri=1432","nofollow":true},{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432"},{"id":2349,"name":"Semantics","url":"https://www.academia.edu/Documents/in/Semantics?f_ri=1432"},{"id":2525,"name":"Language Variation and Change","url":"https://www.academia.edu/Documents/in/Language_Variation_and_Change?f_ri=1432"},{"id":6642,"name":"Language Evolution","url":"https://www.academia.edu/Documents/in/Language_Evolution?f_ri=1432"},{"id":6671,"name":"Syntax","url":"https://www.academia.edu/Documents/in/Syntax?f_ri=1432"},{"id":13769,"name":"Lexical Semantics","url":"https://www.academia.edu/Documents/in/Lexical_Semantics?f_ri=1432"},{"id":19810,"name":"Theoretical Linguistics","url":"https://www.academia.edu/Documents/in/Theoretical_Linguistics?f_ri=1432"},{"id":21548,"name":"Cognitive Neuroscience","url":"https://www.academia.edu/Documents/in/Cognitive_Neuroscience?f_ri=1432"},{"id":33274,"name":"Biology of Language","url":"https://www.academia.edu/Documents/in/Biology_of_Language?f_ri=1432"},{"id":70529,"name":"Indeterminacy","url":"https://www.academia.edu/Documents/in/Indeterminacy?f_ri=1432"},{"id":76428,"name":"Theories of Language and Linguistics","url":"https://www.academia.edu/Documents/in/Theories_of_Language_and_Linguistics?f_ri=1432"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_45055518" data-work_id="45055518" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" rel="nofollow" href="https://www.academia.edu/45055518/Top_10_Cited_Articles_of_AI_2021_International_Journal_of_Artificial_Intelligence_and_Applications_IJAIA_">Top 10 Cited Articles of AI 2021 - International Journal of Artificial Intelligence & Applications (IJAIA)</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_45055518" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.<br /><br />Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Artificial Intelligence & applications.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/45055518" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="a9b126e883080cbcefbfdcbf950694cd" rel="nofollow" data-download="{"attachment_id":65609819,"asset_id":45055518,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/65609819/download_file?st=MTczOTgzODMzOCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="19082339" rel="nofollow" href="https://independent.academia.edu/IjaiaJournal">International Journal of Artificial Intelligence (IJAIA)</a><script data-card-contents-for-user="19082339" type="text/json">{"id":19082339,"first_name":"International Journal of Artificial Intelligence","last_name":"(IJAIA)","domain_name":"independent","page_name":"IjaiaJournal","display_name":"International Journal of Artificial Intelligence (IJAIA)","profile_url":"https://independent.academia.edu/IjaiaJournal?f_ri=1432","photo":"https://0.academia-photos.com/19082339/5301872/160061290/s65_international_journal_of_artificial_intelligence_applications._ijaia_.jpg"}</script></span></span></li><li class="js-paper-rank-work_45055518 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="45055518"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 45055518, container: ".js-paper-rank-work_45055518", }); 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It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.\n\nAuthors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Artificial Intelligence \u0026 applications.","downloadable_attachments":[{"id":65609819,"asset_id":45055518,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":19082339,"first_name":"International Journal of Artificial Intelligence","last_name":"(IJAIA)","domain_name":"independent","page_name":"IjaiaJournal","display_name":"International Journal of Artificial Intelligence (IJAIA)","profile_url":"https://independent.academia.edu/IjaiaJournal?f_ri=1432","photo":"https://0.academia-photos.com/19082339/5301872/160061290/s65_international_journal_of_artificial_intelligence_applications._ijaia_.jpg"}],"research_interests":[{"id":77,"name":"Robotics","url":"https://www.academia.edu/Documents/in/Robotics?f_ri=1432","nofollow":true},{"id":146,"name":"Bioinformatics","url":"https://www.academia.edu/Documents/in/Bioinformatics?f_ri=1432","nofollow":true},{"id":451,"name":"Programming Languages","url":"https://www.academia.edu/Documents/in/Programming_Languages?f_ri=1432","nofollow":true},{"id":464,"name":"Information Retrieval","url":"https://www.academia.edu/Documents/in/Information_Retrieval?f_ri=1432","nofollow":true},{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence?f_ri=1432"},{"id":854,"name":"Computer Vision","url":"https://www.academia.edu/Documents/in/Computer_Vision?f_ri=1432"},{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432"},{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=1432"},{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=1432"},{"id":3414,"name":"Mechatronics","url":"https://www.academia.edu/Documents/in/Mechatronics?f_ri=1432"},{"id":3419,"name":"Multimedia","url":"https://www.academia.edu/Documents/in/Multimedia?f_ri=1432"},{"id":4165,"name":"Fuzzy Logic","url":"https://www.academia.edu/Documents/in/Fuzzy_Logic?f_ri=1432"},{"id":5109,"name":"Pattern Recognition","url":"https://www.academia.edu/Documents/in/Pattern_Recognition?f_ri=1432"},{"id":9134,"name":"Pervasive Computing","url":"https://www.academia.edu/Documents/in/Pervasive_Computing?f_ri=1432"},{"id":11598,"name":"Neural Networks","url":"https://www.academia.edu/Documents/in/Neural_Networks?f_ri=1432"},{"id":12428,"name":"Automatic Control","url":"https://www.academia.edu/Documents/in/Automatic_Control?f_ri=1432"},{"id":17167,"name":"Parallel Processing","url":"https://www.academia.edu/Documents/in/Parallel_Processing?f_ri=1432"},{"id":22615,"name":"Knowledge Representation","url":"https://www.academia.edu/Documents/in/Knowledge_Representation?f_ri=1432"},{"id":84577,"name":"Knowledge-Based Systems","url":"https://www.academia.edu/Documents/in/Knowledge-Based_Systems?f_ri=1432"},{"id":287095,"name":"Knowledge Representation and Reasoning","url":"https://www.academia.edu/Documents/in/Knowledge_Representation_and_Reasoning?f_ri=1432"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_51016843" data-work_id="51016843" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" rel="nofollow" href="https://www.academia.edu/51016843/Call_For_Papers_September_Issue_International_Journal_of_Artificial_Intelligence_and_Applications_IJAIA_">Call For Papers - September Issue - International Journal of Artificial Intelligence & Applications (IJAIA)</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_51016843" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/51016843" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="2f7669cfc14bc93dab8b9f106573ee36" rel="nofollow" data-download="{"attachment_id":90409784,"asset_id":51016843,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/90409784/download_file?st=MTczOTgzODMzOCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="19082339" rel="nofollow" href="https://independent.academia.edu/IjaiaJournal">International Journal of Artificial Intelligence (IJAIA)</a><script data-card-contents-for-user="19082339" type="text/json">{"id":19082339,"first_name":"International Journal of Artificial Intelligence","last_name":"(IJAIA)","domain_name":"independent","page_name":"IjaiaJournal","display_name":"International Journal of Artificial Intelligence (IJAIA)","profile_url":"https://independent.academia.edu/IjaiaJournal?f_ri=1432","photo":"https://0.academia-photos.com/19082339/5301872/160061290/s65_international_journal_of_artificial_intelligence_applications._ijaia_.jpg"}</script></span></span></li><li class="js-paper-rank-work_51016843 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="51016843"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 51016843, container: ".js-paper-rank-work_51016843", }); 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u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/4107831/The_Layers_of_Research_Design">The Layers of Research Design</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Within this article we use the metaphor of the “Research Onion” (Saunders et al., 2012: 128) to illustrate how these final elements (the core of the research onion) need to be considered in relation to other design elements (the outer... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_4107831" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Within this article we use the metaphor of the “Research Onion” (Saunders et al., 2012: 128) to illustrate how these final elements (the core of the research onion) need to be considered in relation to other design elements (the outer layers of the research onion).  It is the researcher’s understandings and associated decisions in relation to these outer layers that provide the context and boundaries within which data collection techniques and analysis procedures will be selected.  <br /> <br /> Please note, this is the published version and has been uploaded with permission from Karen Moxom (ANLP) <br /> <br />Please note, with Pearson's permission I have uploaded the proofs of chapter 4 for the 7th edition of Research Methods for Business Students (published in August 2015)  to academia.edu.  This contains the latest version of the research onionademia.edu.  The direct link is: <a href="https://www.academia.edu/13016419/Research_Methods_for_Business_Students_Chapter_4_Understanding_research_philosophy_and_approaches_to_theory_development_" rel="nofollow">https://www.academia.edu/13016419/Research_Methods_for_Business_Students_Chapter_4_Understanding_research_philosophy_and_approaches_to_theory_development_</a></div></div></div><ul 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(Saunders et al., 2012: 128) to illustrate how these final elements (the core of the research onion) need to be considered in relation to other design elements (the outer layers of the research onion). It is the researcher’s understandings and associated decisions in relation to these outer layers that provide the context and boundaries within which data collection techniques and analysis procedures will be selected. \r\n\r\n Please note, this is the published version and has been uploaded with permission from Karen Moxom (ANLP) \r\n\r\nPlease note, with Pearson's permission I have uploaded the proofs of chapter 4 for the 7th edition of Research Methods for Business Students (published in August 2015) to academia.edu. This contains the latest version of the research onionademia.edu. The direct link is: https://www.academia.edu/13016419/Research_Methods_for_Business_Students_Chapter_4_Understanding_research_philosophy_and_approaches_to_theory_development_","downloadable_attachments":[{"id":32495174,"asset_id":4107831,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":4212726,"first_name":"Mark","last_name":"Saunders","domain_name":"birmingham","page_name":"MarkSaunders","display_name":"Mark N K Saunders","profile_url":"https://birmingham.academia.edu/MarkSaunders?f_ri=1432","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence?f_ri=1432","nofollow":true},{"id":1230,"name":"Social Research Methods and Methodology","url":"https://www.academia.edu/Documents/in/Social_Research_Methods_and_Methodology?f_ri=1432","nofollow":true},{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true},{"id":1558,"name":"Research Methods and Methodology","url":"https://www.academia.edu/Documents/in/Research_Methods_and_Methodology?f_ri=1432","nofollow":true},{"id":2065,"name":"Research Methodology","url":"https://www.academia.edu/Documents/in/Research_Methodology?f_ri=1432"},{"id":3136,"name":"Qualitative methodology","url":"https://www.academia.edu/Documents/in/Qualitative_methodology?f_ri=1432"},{"id":3285,"name":"Qualitative Methods","url":"https://www.academia.edu/Documents/in/Qualitative_Methods?f_ri=1432"},{"id":11847,"name":"Quantitative Research","url":"https://www.academia.edu/Documents/in/Quantitative_Research?f_ri=1432"},{"id":19870,"name":"Research","url":"https://www.academia.edu/Documents/in/Research?f_ri=1432"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_76236760" data-work_id="76236760" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/76236760/Normalized_names_for_clinical_drugs_RxNorm_at_6_years">Normalized names for clinical drugs: RxNorm at 6 years</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Objective In the 6 years since the National Library of Medicine began monthly releases of RxNorm, RxNorm has become a central resource for communicating about clinical drugs and supporting interoperation between drug vocabularies.... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_76236760" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Objective In the 6 years since the National Library of Medicine began monthly releases of RxNorm, RxNorm has become a central resource for communicating about clinical drugs and supporting interoperation between drug vocabularies. Materials and methods Built on the idea of a normalized name for a medication at a given level of abstraction, RxNorm provides a set of names and relationships based on 11 different external source vocabularies. The standard model enables decision support to take place for a variety of uses at the appropriate level of abstraction. With the incorporation of National Drug File Reference Terminology (NDF-RT) from the Veterans Administration, even more sophisticated decision support has become possible. Discussion While related products such as RxTerms, RxNav, MyMedicationList, and MyRxPad have been recognized as helpful for various uses, tasks such as identifying exactly what is and is not on the market remain a challenge.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/76236760" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="781752c30b83ee18afbeffee9241f55b" rel="nofollow" data-download="{"attachment_id":84015508,"asset_id":76236760,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/84015508/download_file?st=MTczOTgzODMzOCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="3624226" href="https://thebody.academia.edu/JohnKilbourne">John Kilbourne</a><script data-card-contents-for-user="3624226" type="text/json">{"id":3624226,"first_name":"John","last_name":"Kilbourne","domain_name":"thebody","page_name":"JohnKilbourne","display_name":"John Kilbourne","profile_url":"https://thebody.academia.edu/JohnKilbourne?f_ri=1432","photo":"https://0.academia-photos.com/3624226/74677209/63177334/s65_john.kilbourne.png"}</script></span></span></li><li class="js-paper-rank-work_76236760 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="76236760"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 76236760, container: ".js-paper-rank-work_76236760", }); 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$(".js-view-count[data-work-id=76236760]").text(description); $(".js-view-count-work_76236760").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_76236760").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="76236760"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">19</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="48" rel="nofollow" href="https://www.academia.edu/Documents/in/Engineering">Engineering</a>, <script data-card-contents-for-ri="48" type="text/json">{"id":48,"name":"Engineering","url":"https://www.academia.edu/Documents/in/Engineering?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="422" rel="nofollow" href="https://www.academia.edu/Documents/in/Computer_Science">Computer Science</a>, <script data-card-contents-for-ri="422" type="text/json">{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="1432" rel="nofollow" href="https://www.academia.edu/Documents/in/Natural_Language_Processing">Natural Language Processing</a>, <script data-card-contents-for-ri="1432" type="text/json">{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="26327" rel="nofollow" href="https://www.academia.edu/Documents/in/Medicine">Medicine</a><script data-card-contents-for-ri="26327" type="text/json">{"id":26327,"name":"Medicine","url":"https://www.academia.edu/Documents/in/Medicine?f_ri=1432","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=76236760]'), work: {"id":76236760,"title":"Normalized names for clinical drugs: RxNorm at 6 years","created_at":"2022-04-12T08:18:09.618-07:00","url":"https://www.academia.edu/76236760/Normalized_names_for_clinical_drugs_RxNorm_at_6_years?f_ri=1432","dom_id":"work_76236760","summary":"Objective In the 6 years since the National Library of Medicine began monthly releases of RxNorm, RxNorm has become a central resource for communicating about clinical drugs and supporting interoperation between drug vocabularies. Materials and methods Built on the idea of a normalized name for a medication at a given level of abstraction, RxNorm provides a set of names and relationships based on 11 different external source vocabularies. The standard model enables decision support to take place for a variety of uses at the appropriate level of abstraction. With the incorporation of National Drug File Reference Terminology (NDF-RT) from the Veterans Administration, even more sophisticated decision support has become possible. Discussion While related products such as RxTerms, RxNav, MyMedicationList, and MyRxPad have been recognized as helpful for various uses, tasks such as identifying exactly what is and is not on the market remain a challenge.","downloadable_attachments":[{"id":84015508,"asset_id":76236760,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":3624226,"first_name":"John","last_name":"Kilbourne","domain_name":"thebody","page_name":"JohnKilbourne","display_name":"John Kilbourne","profile_url":"https://thebody.academia.edu/JohnKilbourne?f_ri=1432","photo":"https://0.academia-photos.com/3624226/74677209/63177334/s65_john.kilbourne.png"}],"research_interests":[{"id":48,"name":"Engineering","url":"https://www.academia.edu/Documents/in/Engineering?f_ri=1432","nofollow":true},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=1432","nofollow":true},{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true},{"id":26327,"name":"Medicine","url":"https://www.academia.edu/Documents/in/Medicine?f_ri=1432","nofollow":true},{"id":36299,"name":"Distributed Systems","url":"https://www.academia.edu/Documents/in/Distributed_Systems?f_ri=1432"},{"id":51860,"name":"Ontologies","url":"https://www.academia.edu/Documents/in/Ontologies?f_ri=1432"},{"id":91652,"name":"Drug Information Services","url":"https://www.academia.edu/Documents/in/Drug_Information_Services?f_ri=1432"},{"id":98134,"name":"United States","url":"https://www.academia.edu/Documents/in/United_States?f_ri=1432"},{"id":130616,"name":"Standard Model","url":"https://www.academia.edu/Documents/in/Standard_Model?f_ri=1432"},{"id":133934,"name":"Levels of Abstraction","url":"https://www.academia.edu/Documents/in/Levels_of_Abstraction?f_ri=1432"},{"id":149081,"name":"Decision Support","url":"https://www.academia.edu/Documents/in/Decision_Support?f_ri=1432"},{"id":161176,"name":"The","url":"https://www.academia.edu/Documents/in/The?f_ri=1432"},{"id":235015,"name":"Model Based Clinical Decision Support Systems","url":"https://www.academia.edu/Documents/in/Model_Based_Clinical_Decision_Support_Systems?f_ri=1432"},{"id":255094,"name":"Computer User Interface Design","url":"https://www.academia.edu/Documents/in/Computer_User_Interface_Design?f_ri=1432"},{"id":255453,"name":"Information Storage and Retrieval","url":"https://www.academia.edu/Documents/in/Information_Storage_and_Retrieval?f_ri=1432"},{"id":349137,"name":"Data Models","url":"https://www.academia.edu/Documents/in/Data_Models?f_ri=1432"},{"id":1003397,"name":"Knowledge Representations","url":"https://www.academia.edu/Documents/in/Knowledge_Representations?f_ri=1432"},{"id":2380004,"name":"Pharmaceutical preparations","url":"https://www.academia.edu/Documents/in/Pharmaceutical_preparations?f_ri=1432"},{"id":3763225,"name":"Medical and Health Sciences","url":"https://www.academia.edu/Documents/in/Medical_and_Health_Sciences?f_ri=1432"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_70114587" data-work_id="70114587" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/70114587/Literature_review_on_patient_friendly_documentation_systems">Literature review on patient-friendly documentation systems</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Description: English medical, intensive care data From <a href="http://i3p-class.itc.it/projects_scheda.asp?id=14" rel="nofollow">http://i3p-class.itc.it/projects_scheda.asp?id=14</a>: "MAGIC is an intelligent multimedia presentation system for the medical domain. After a patient has heart surgery, the physicians in... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_70114587" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Description: English medical, intensive care data From <a href="http://i3p-class.itc.it/projects_scheda.asp?id=14" rel="nofollow">http://i3p-class.itc.it/projects_scheda.asp?id=14</a>: "MAGIC is an intelligent multimedia presentation system for the medical domain. After a patient has heart surgery, the physicians in the operating room (or) must inform the caregivers in the intensive care unit (icu) what happened during the surgery in order to prepare for the patient when he/she arrives in the ICU. MAGIC replaces the OR physicians in this scenario by presenting similar information using coordinated text, speech and graphics." Research Focus: Integration and coordination of graphics, speech and text. Evaluation.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/70114587" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="5c5ba75746a32a0dc2559ee0228e2bc8" rel="nofollow" data-download="{"attachment_id":79978829,"asset_id":70114587,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/79978829/download_file?st=MTczOTgzODMzOCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="453349" href="https://open.academia.edu/ClaraMancini">Clara Mancini</a><script data-card-contents-for-user="453349" type="text/json">{"id":453349,"first_name":"Clara","last_name":"Mancini","domain_name":"open","page_name":"ClaraMancini","display_name":"Clara Mancini","profile_url":"https://open.academia.edu/ClaraMancini?f_ri=1432","photo":"https://0.academia-photos.com/453349/1570394/60868384/s65_clara.mancini.png"}</script></span></span></li><li class="js-paper-rank-work_70114587 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="70114587"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 70114587, container: ".js-paper-rank-work_70114587", }); 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$(".js-view-count[data-work-id=70114587]").text(description); $(".js-view-count-work_70114587").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_70114587").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="70114587"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">11</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="1212" rel="nofollow" href="https://www.academia.edu/Documents/in/Medical_Informatics">Medical Informatics</a>, <script data-card-contents-for-ri="1212" type="text/json">{"id":1212,"name":"Medical Informatics","url":"https://www.academia.edu/Documents/in/Medical_Informatics?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="1432" rel="nofollow" href="https://www.academia.edu/Documents/in/Natural_Language_Processing">Natural Language Processing</a>, <script data-card-contents-for-ri="1432" type="text/json">{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="2185" rel="nofollow" href="https://www.academia.edu/Documents/in/Natural_Language_Generation">Natural Language Generation</a>, <script data-card-contents-for-ri="2185" type="text/json">{"id":2185,"name":"Natural Language Generation","url":"https://www.academia.edu/Documents/in/Natural_Language_Generation?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="44293" rel="nofollow" href="https://www.academia.edu/Documents/in/Literature_Review">Literature Review</a><script data-card-contents-for-ri="44293" type="text/json">{"id":44293,"name":"Literature Review","url":"https://www.academia.edu/Documents/in/Literature_Review?f_ri=1432","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=70114587]'), work: {"id":70114587,"title":"Literature review on patient-friendly documentation systems","created_at":"2022-01-31T00:26:30.231-08:00","url":"https://www.academia.edu/70114587/Literature_review_on_patient_friendly_documentation_systems?f_ri=1432","dom_id":"work_70114587","summary":"Description: English medical, intensive care data From http://i3p-class.itc.it/projects_scheda.asp?id=14: \"MAGIC is an intelligent multimedia presentation system for the medical domain. After a patient has heart surgery, the physicians in the operating room (or) must inform the caregivers in the intensive care unit (icu) what happened during the surgery in order to prepare for the patient when he/she arrives in the ICU. MAGIC replaces the OR physicians in this scenario by presenting similar information using coordinated text, speech and graphics.\" Research Focus: Integration and coordination of graphics, speech and text. Evaluation.","downloadable_attachments":[{"id":79978829,"asset_id":70114587,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":453349,"first_name":"Clara","last_name":"Mancini","domain_name":"open","page_name":"ClaraMancini","display_name":"Clara Mancini","profile_url":"https://open.academia.edu/ClaraMancini?f_ri=1432","photo":"https://0.academia-photos.com/453349/1570394/60868384/s65_clara.mancini.png"}],"research_interests":[{"id":1212,"name":"Medical Informatics","url":"https://www.academia.edu/Documents/in/Medical_Informatics?f_ri=1432","nofollow":true},{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true},{"id":2185,"name":"Natural Language Generation","url":"https://www.academia.edu/Documents/in/Natural_Language_Generation?f_ri=1432","nofollow":true},{"id":44293,"name":"Literature Review","url":"https://www.academia.edu/Documents/in/Literature_Review?f_ri=1432","nofollow":true},{"id":90883,"name":"Internet Relay Chat","url":"https://www.academia.edu/Documents/in/Internet_Relay_Chat?f_ri=1432"},{"id":160532,"name":"NLG","url":"https://www.academia.edu/Documents/in/NLG?f_ri=1432"},{"id":164510,"name":"Medical Documentation","url":"https://www.academia.edu/Documents/in/Medical_Documentation?f_ri=1432"},{"id":164513,"name":"Internet as Corpus","url":"https://www.academia.edu/Documents/in/Internet_as_Corpus?f_ri=1432"},{"id":164515,"name":"Web as Corpus","url":"https://www.academia.edu/Documents/in/Web_as_Corpus?f_ri=1432"},{"id":164517,"name":"Irc as Corpus","url":"https://www.academia.edu/Documents/in/Irc_as_Corpus?f_ri=1432"},{"id":164519,"name":"Medical Nlp","url":"https://www.academia.edu/Documents/in/Medical_Nlp?f_ri=1432"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_41304936" data-work_id="41304936" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/41304936/An_intelligent_system_on_knowledge_generation_and_communication_about_flooding">An intelligent system on knowledge generation and communication about flooding</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Communities are at risk from extreme events and natural disasters that can lead to dangerous situations for residents. Improving resilience by helping people learn how to better prepare for, recover from, and adapt to disasters is... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_41304936" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Communities are at risk from extreme events and natural disasters that can lead to dangerous situations for residents. Improving resilience by helping people learn how to better prepare for, recover from, and adapt to disasters is critical to reduce the impacts of these extreme events. This project presents an intelligent system, Flood AI, designed to improve societal preparedness for flooding by providing a knowledge engine that uses voice recognition, artificial intelligence, and natural language processing based on a generalized ontology for disasters with a primary focus on flooding. The knowledge engine uses flood ontology to connect user input to relevant knowledge discovery channels on flooding by developing a data acquisition and processing framework using environmental observations, forecast models, and knowledge bases. The framework's communication channels include web-based systems, agent-based chatbots, smartphone applications, automated web workflows, and smart home devices, opening the knowledge discovery for flooding to many unique use cases.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/41304936" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="c6caed474364c4f2a21df229c24fb5e8" rel="nofollow" data-download="{"attachment_id":61517196,"asset_id":41304936,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/61517196/download_file?st=MTczOTgzODMzOCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="121186912" href="https://uiowa.academia.edu/YSermet">Yusuf Sermet</a><script data-card-contents-for-user="121186912" type="text/json">{"id":121186912,"first_name":"Yusuf","last_name":"Sermet","domain_name":"uiowa","page_name":"YSermet","display_name":"Yusuf Sermet","profile_url":"https://uiowa.academia.edu/YSermet?f_ri=1432","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_41304936 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="41304936"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 41304936, container: ".js-paper-rank-work_41304936", }); 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$(".js-view-count[data-work-id=41304936]").text(description); $(".js-view-count-work_41304936").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_41304936").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="41304936"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">8</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="37" rel="nofollow" href="https://www.academia.edu/Documents/in/Information_Systems">Information Systems</a>, <script data-card-contents-for-ri="37" type="text/json">{"id":37,"name":"Information Systems","url":"https://www.academia.edu/Documents/in/Information_Systems?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="465" rel="nofollow" href="https://www.academia.edu/Documents/in/Artificial_Intelligence">Artificial Intelligence</a>, <script data-card-contents-for-ri="465" type="text/json">{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="1432" rel="nofollow" href="https://www.academia.edu/Documents/in/Natural_Language_Processing">Natural Language Processing</a>, <script data-card-contents-for-ri="1432" type="text/json">{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="5105" rel="nofollow" href="https://www.academia.edu/Documents/in/Ontology_Computer_Science_">Ontology (Computer Science)</a><script data-card-contents-for-ri="5105" type="text/json">{"id":5105,"name":"Ontology (Computer Science)","url":"https://www.academia.edu/Documents/in/Ontology_Computer_Science_?f_ri=1432","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=41304936]'), work: {"id":41304936,"title":"An intelligent system on knowledge generation and communication about flooding","created_at":"2019-12-14T23:53:57.982-08:00","url":"https://www.academia.edu/41304936/An_intelligent_system_on_knowledge_generation_and_communication_about_flooding?f_ri=1432","dom_id":"work_41304936","summary":"Communities are at risk from extreme events and natural disasters that can lead to dangerous situations for residents. Improving resilience by helping people learn how to better prepare for, recover from, and adapt to disasters is critical to reduce the impacts of these extreme events. This project presents an intelligent system, Flood AI, designed to improve societal preparedness for flooding by providing a knowledge engine that uses voice recognition, artificial intelligence, and natural language processing based on a generalized ontology for disasters with a primary focus on flooding. The knowledge engine uses flood ontology to connect user input to relevant knowledge discovery channels on flooding by developing a data acquisition and processing framework using environmental observations, forecast models, and knowledge bases. The framework's communication channels include web-based systems, agent-based chatbots, smartphone applications, automated web workflows, and smart home devices, opening the knowledge discovery for flooding to many unique use cases.","downloadable_attachments":[{"id":61517196,"asset_id":41304936,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":121186912,"first_name":"Yusuf","last_name":"Sermet","domain_name":"uiowa","page_name":"YSermet","display_name":"Yusuf Sermet","profile_url":"https://uiowa.academia.edu/YSermet?f_ri=1432","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":37,"name":"Information Systems","url":"https://www.academia.edu/Documents/in/Information_Systems?f_ri=1432","nofollow":true},{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence?f_ri=1432","nofollow":true},{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true},{"id":5105,"name":"Ontology (Computer Science)","url":"https://www.academia.edu/Documents/in/Ontology_Computer_Science_?f_ri=1432","nofollow":true},{"id":10977,"name":"Intelligent Systems","url":"https://www.academia.edu/Documents/in/Intelligent_Systems?f_ri=1432"},{"id":15582,"name":"Disaster Management","url":"https://www.academia.edu/Documents/in/Disaster_Management?f_ri=1432"},{"id":45972,"name":"Hydroinformatics","url":"https://www.academia.edu/Documents/in/Hydroinformatics?f_ri=1432"},{"id":1009312,"name":"Geographic Information Systems (GIS)","url":"https://www.academia.edu/Documents/in/Geographic_Information_Systems_GIS_?f_ri=1432"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_13734710 coauthored" data-work_id="13734710" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/13734710/Efficient_Classification_of_Clinical_Reports_Utilizing_Natural_Language_Processing">Efficient Classification of Clinical Reports Utilizing Natural Language Processing</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The recent emphasis on health information technology has highlighted the importance of leveraging the large amount of electronic clinical data to help guide medical decision-making. Developing such clinical decision aids requires manual... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_13734710" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The recent emphasis on health information technology has highlighted the importance of leveraging the large amount of electronic clinical data to help guide medical decision-making. Developing such clinical decision aids requires manual review of many past patient reports in order to generate a good predictive model. In this research, we investigate classification of clinical reports using natural language processing (NLP). The proposed system uses NLP to generate structured output from computed tomography (CT) reports and then machine learning techniques to code for the presence of clinically important injuries for traumatic orbital fracture victims. Our results show that NLP improves upon raw text classification results.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/13734710" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="da9fca09bdddc61b026412c6b0abbc5d" rel="nofollow" data-download="{"attachment_id":45008275,"asset_id":13734710,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/45008275/download_file?st=MTczOTgzODMzOCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="32858149" href="https://harborucla.academia.edu/KabirYadav">Kabir Yadav</a><script data-card-contents-for-user="32858149" type="text/json">{"id":32858149,"first_name":"Kabir","last_name":"Yadav","domain_name":"harborucla","page_name":"KabirYadav","display_name":"Kabir Yadav","profile_url":"https://harborucla.academia.edu/KabirYadav?f_ri=1432","photo":"https://0.academia-photos.com/32858149/10028641/11186167/s65_kabir.yadav.jpg"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-13734710">+1</span><div class="hidden js-additional-users-13734710"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://jhuapl.academia.edu/EfsunSariogluKayi">Efsun Sarioglu Kayi</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-13734710'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-13734710').html(); 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container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_13734710 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="13734710"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 13734710; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=13734710]").text(description); $(".js-view-count-work_13734710").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_13734710").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="13734710"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">3</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="1432" rel="nofollow" href="https://www.academia.edu/Documents/in/Natural_Language_Processing">Natural Language Processing</a>, <script data-card-contents-for-ri="1432" type="text/json">{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="2008" rel="nofollow" href="https://www.academia.edu/Documents/in/Machine_Learning">Machine Learning</a>, <script data-card-contents-for-ri="2008" type="text/json">{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="4095" rel="nofollow" href="https://www.academia.edu/Documents/in/Classification_Machine_Learning_">Classification (Machine Learning)</a><script data-card-contents-for-ri="4095" type="text/json">{"id":4095,"name":"Classification (Machine Learning)","url":"https://www.academia.edu/Documents/in/Classification_Machine_Learning_?f_ri=1432","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=13734710]'), work: {"id":13734710,"title":"Efficient Classification of Clinical Reports Utilizing Natural Language Processing","created_at":"2015-07-07T00:03:19.496-07:00","url":"https://www.academia.edu/13734710/Efficient_Classification_of_Clinical_Reports_Utilizing_Natural_Language_Processing?f_ri=1432","dom_id":"work_13734710","summary":"The recent emphasis on health information technology has highlighted the importance of leveraging the large amount of electronic clinical data to help guide medical decision-making. Developing such clinical decision aids requires manual review of many past patient reports in order to generate a good predictive model. In this research, we investigate classification of clinical reports using natural language processing (NLP). The proposed system uses NLP to generate structured output from computed tomography (CT) reports and then machine learning techniques to code for the presence of clinically important injuries for traumatic orbital fracture victims. Our results show that NLP improves upon raw text classification results.","downloadable_attachments":[{"id":45008275,"asset_id":13734710,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":32858149,"first_name":"Kabir","last_name":"Yadav","domain_name":"harborucla","page_name":"KabirYadav","display_name":"Kabir Yadav","profile_url":"https://harborucla.academia.edu/KabirYadav?f_ri=1432","photo":"https://0.academia-photos.com/32858149/10028641/11186167/s65_kabir.yadav.jpg"},{"id":8358110,"first_name":"Efsun","last_name":"Sarioglu Kayi","domain_name":"jhuapl","page_name":"EfsunSariogluKayi","display_name":"Efsun Sarioglu Kayi","profile_url":"https://jhuapl.academia.edu/EfsunSariogluKayi?f_ri=1432","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true},{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=1432","nofollow":true},{"id":4095,"name":"Classification (Machine Learning)","url":"https://www.academia.edu/Documents/in/Classification_Machine_Learning_?f_ri=1432","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_17942986" data-work_id="17942986" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/17942986/Recent_Advances_in_Literature_Based_Discovery">Recent Advances in Literature Based Discovery</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Literature Based Discovery (LBD) is a process that searches for hidden and important connections among information embedded in published literature. Employing techniques from Information Retrieval and Natural Language Processing, LBD has... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_17942986" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Literature Based Discovery (LBD) is a process that searches for hidden and important connections among information embedded in published literature. Employing techniques from Information Retrieval and Natural Language Processing, LBD has potential for widespread application yet is currently implemented primarily in the medical domain. This article examines several published LBD systems, comparing their descriptions of domain and input data, techniques</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/17942986" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="dbe9bdd64e35ddc96639f742c5924747" rel="nofollow" data-download="{"attachment_id":39791232,"asset_id":17942986,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/39791232/download_file?st=MTczOTgzODMzOCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="37767897" href="https://marmara.academia.edu/MuratGaniz">Murat C Ganiz</a><script data-card-contents-for-user="37767897" type="text/json">{"id":37767897,"first_name":"Murat","last_name":"Ganiz","domain_name":"marmara","page_name":"MuratGaniz","display_name":"Murat C Ganiz","profile_url":"https://marmara.academia.edu/MuratGaniz?f_ri=1432","photo":"https://0.academia-photos.com/37767897/11087847/12373503/s65_murat.ganiz.jpg"}</script></span></span></li><li class="js-paper-rank-work_17942986 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="17942986"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 17942986, container: ".js-paper-rank-work_17942986", }); });</script></li><li class="js-percentile-work_17942986 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 17942986; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_17942986"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_17942986 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="17942986"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 17942986; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=17942986]").text(description); $(".js-view-count-work_17942986").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_17942986").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="17942986"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">9</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="464" rel="nofollow" href="https://www.academia.edu/Documents/in/Information_Retrieval">Information Retrieval</a>, <script data-card-contents-for-ri="464" type="text/json">{"id":464,"name":"Information Retrieval","url":"https://www.academia.edu/Documents/in/Information_Retrieval?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="1432" rel="nofollow" href="https://www.academia.edu/Documents/in/Natural_Language_Processing">Natural Language Processing</a>, <script data-card-contents-for-ri="1432" type="text/json">{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="2009" rel="nofollow" href="https://www.academia.edu/Documents/in/Data_Mining">Data Mining</a>, <script data-card-contents-for-ri="2009" type="text/json">{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="313752" rel="nofollow" href="https://www.academia.edu/Documents/in/Gold_Standard">Gold Standard</a><script data-card-contents-for-ri="313752" type="text/json">{"id":313752,"name":"Gold Standard","url":"https://www.academia.edu/Documents/in/Gold_Standard?f_ri=1432","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=17942986]'), work: {"id":17942986,"title":"Recent Advances in Literature Based Discovery","created_at":"2015-11-07T23:55:20.468-08:00","url":"https://www.academia.edu/17942986/Recent_Advances_in_Literature_Based_Discovery?f_ri=1432","dom_id":"work_17942986","summary":"Literature Based Discovery (LBD) is a process that searches for hidden and important connections among information embedded in published literature. Employing techniques from Information Retrieval and Natural Language Processing, LBD has potential for widespread application yet is currently implemented primarily in the medical domain. This article examines several published LBD systems, comparing their descriptions of domain and input data, techniques","downloadable_attachments":[{"id":39791232,"asset_id":17942986,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":37767897,"first_name":"Murat","last_name":"Ganiz","domain_name":"marmara","page_name":"MuratGaniz","display_name":"Murat C Ganiz","profile_url":"https://marmara.academia.edu/MuratGaniz?f_ri=1432","photo":"https://0.academia-photos.com/37767897/11087847/12373503/s65_murat.ganiz.jpg"}],"research_interests":[{"id":464,"name":"Information Retrieval","url":"https://www.academia.edu/Documents/in/Information_Retrieval?f_ri=1432","nofollow":true},{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true},{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=1432","nofollow":true},{"id":313752,"name":"Gold Standard","url":"https://www.academia.edu/Documents/in/Gold_Standard?f_ri=1432","nofollow":true},{"id":414770,"name":"Scientific Knowledge","url":"https://www.academia.edu/Documents/in/Scientific_Knowledge?f_ri=1432"},{"id":428860,"name":"Evaluation Methodology","url":"https://www.academia.edu/Documents/in/Evaluation_Methodology?f_ri=1432"},{"id":567610,"name":"Literature Based Discovery","url":"https://www.academia.edu/Documents/in/Literature_Based_Discovery?f_ri=1432"},{"id":1157221,"name":"Bibliographic Database","url":"https://www.academia.edu/Documents/in/Bibliographic_Database?f_ri=1432"},{"id":1944655,"name":"Information Embedding","url":"https://www.academia.edu/Documents/in/Information_Embedding?f_ri=1432"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_64417565" data-work_id="64417565" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/64417565/Improving_Hate_Speech_Detection_of_Urdu_Tweets_Using_Sentiment_Analysis">Improving Hate Speech Detection of Urdu Tweets Using Sentiment Analysis</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Sentiment Analysis is a technique that is being used abundantly nowadays for customer reviews analysis, popularity analysis of electoral candidates, hate speech detection and similar applications. Sentiment analysis on tweets encounters... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_64417565" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Sentiment Analysis is a technique that is being used abundantly nowadays for customer reviews analysis, popularity analysis of electoral candidates, hate speech detection and similar applications. Sentiment analysis on tweets encounters challenges such as highly skewed classes, high dimensional feature vectors and highly sparse data. In this study, we have analyzed the improvement achieved by successively addressing these problems in order to determine their severity for sentiment analysis of tweets. Firstly, we prepared a comprehensive data set consisting of Urdu Tweets for sentiment analysis-based hate speech detection. To improve the performance of the sentiment classifier, we employed dynamic stop words filtering, Variable Global Feature Selection Scheme (VGFSS) and Synthetic Minority Optimization Technique (SMOTE) to handle the sparsity, dimensionality and class imbalance problems respectively. We used two machine learning algorithms i.e., Support Vector Machines (SVM) and Multinomial Naïve Bayes’ (MNB) for investigating performance in our experiments. Our results show that addressing class skew along with alleviating the high dimensionality problem brings about the maximum improvement in the overall performance of the sentiment analysis-based hate speech detection.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/64417565" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="2c050e31f85642245cda44abae073d5a" rel="nofollow" data-download="{"attachment_id":76503536,"asset_id":64417565,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/76503536/download_file?st=MTczOTgzODMzOCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="43445117" href="https://uet.academia.edu/SaharRauf">Sahar Rauf</a><script data-card-contents-for-user="43445117" type="text/json">{"id":43445117,"first_name":"Sahar","last_name":"Rauf","domain_name":"uet","page_name":"SaharRauf","display_name":"Sahar Rauf","profile_url":"https://uet.academia.edu/SaharRauf?f_ri=1432","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_64417565 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="64417565"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 64417565, container: ".js-paper-rank-work_64417565", }); });</script></li><li class="js-percentile-work_64417565 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 64417565; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_64417565"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_64417565 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="64417565"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 64417565; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=64417565]").text(description); $(".js-view-count-work_64417565").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_64417565").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="64417565"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">6</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="422" rel="nofollow" href="https://www.academia.edu/Documents/in/Computer_Science">Computer Science</a>, <script data-card-contents-for-ri="422" type="text/json">{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="1432" rel="nofollow" href="https://www.academia.edu/Documents/in/Natural_Language_Processing">Natural Language Processing</a>, <script data-card-contents-for-ri="1432" type="text/json">{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="2008" rel="nofollow" href="https://www.academia.edu/Documents/in/Machine_Learning">Machine Learning</a>, <script data-card-contents-for-ri="2008" type="text/json">{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="2009" rel="nofollow" href="https://www.academia.edu/Documents/in/Data_Mining">Data Mining</a><script data-card-contents-for-ri="2009" type="text/json">{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=1432","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=64417565]'), work: {"id":64417565,"title":"Improving Hate Speech Detection of Urdu Tweets Using Sentiment Analysis","created_at":"2021-12-15T22:21:43.522-08:00","url":"https://www.academia.edu/64417565/Improving_Hate_Speech_Detection_of_Urdu_Tweets_Using_Sentiment_Analysis?f_ri=1432","dom_id":"work_64417565","summary":"Sentiment Analysis is a technique that is being used abundantly nowadays for customer reviews analysis, popularity analysis of electoral candidates, hate speech detection and similar applications. Sentiment analysis on tweets encounters challenges such as highly skewed classes, high dimensional feature vectors and highly sparse data. In this study, we have analyzed the improvement achieved by successively addressing these problems in order to determine their severity for sentiment analysis of tweets. Firstly, we prepared a comprehensive data set consisting of Urdu Tweets for sentiment analysis-based hate speech detection. To improve the performance of the sentiment classifier, we employed dynamic stop words filtering, Variable Global Feature Selection Scheme (VGFSS) and Synthetic Minority Optimization Technique (SMOTE) to handle the sparsity, dimensionality and class imbalance problems respectively. We used two machine learning algorithms i.e., Support Vector Machines (SVM) and Multinomial Naïve Bayes’ (MNB) for investigating performance in our experiments. Our results show that addressing class skew along with alleviating the high dimensionality problem brings about the maximum improvement in the overall performance of the sentiment analysis-based hate speech detection.","downloadable_attachments":[{"id":76503536,"asset_id":64417565,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":43445117,"first_name":"Sahar","last_name":"Rauf","domain_name":"uet","page_name":"SaharRauf","display_name":"Sahar Rauf","profile_url":"https://uet.academia.edu/SaharRauf?f_ri=1432","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=1432","nofollow":true},{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true},{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=1432","nofollow":true},{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=1432","nofollow":true},{"id":5379,"name":"Sentiment Analysis","url":"https://www.academia.edu/Documents/in/Sentiment_Analysis?f_ri=1432"},{"id":3581417,"name":"Hate Speech Detection ","url":"https://www.academia.edu/Documents/in/Hate_Speech_Detection?f_ri=1432"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_82099702" data-work_id="82099702" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/82099702/Cyberbullying_Detection_using_Natural_Language_Processing">Cyberbullying Detection using Natural Language Processing</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Around the world, the use of the Internet and social media has increased exponentially, and they have become an integral part of daily life. It allows people to share their thoughts, feelings, and ideas with their loved ones through the... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_82099702" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Around the world, the use of the Internet and social media has increased exponentially, and they have become an integral part of daily life. It allows people to share their thoughts, feelings, and ideas with their loved ones through the Internet and social media. But with social networking sites becoming more popular, cyberbullying is on the rise. Using technology as a medium to bully someone is known as Cyberbullying. The Internet can be a source of abusive and harmful content and cause harm to others. Social networking sites provide a great medium for harassment, bullies, and youngsters who use these sites are vulnerable to attacks. Bullying can have long-term effects on adolescents' ability to socialize and build lasting friendships Victims of cyberbullying often feel humiliated. social media users often can hide their identity, which helps misuse the available features. The use of offensive language has become one of the most popular issues on social networking. Text containing any form of abusive conduct that displays acts intended to hurt others is offensive language. Cyberbullying frequently leads to serious mental and physical distress, particularly for women and children, and sometimes forces them to commit suicide. The purpose of this project is to develop a technique that is effective to detect and avoid cyberbullying on social networking sites we are using Natural Language Processing and other machine learning algorithms. The dataset that we used for this project was collected from Kaggle, it contains data from Twitter that is then labeled to train the algorithm. Several classifiers are used to train and recognize bullying actions. The evaluation of the proposed Model for cyberbullying dataset shows that Logistic Regression performs better and achieves good accuracy than SVM, Ransom forest, Naive-Bayes, and Xgboost algorithm.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/82099702" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="92289c0401201b40ce1354eb074383cb" rel="nofollow" data-download="{"attachment_id":87914095,"asset_id":82099702,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/87914095/download_file?st=MTczOTgzODMzOCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="6079060" href="https://independent.academia.edu/IJRASETPublication">IJRASET Publication</a><script data-card-contents-for-user="6079060" type="text/json">{"id":6079060,"first_name":"IJRASET","last_name":"Publication","domain_name":"independent","page_name":"IJRASETPublication","display_name":"IJRASET Publication","profile_url":"https://independent.academia.edu/IJRASETPublication?f_ri=1432","photo":"https://0.academia-photos.com/6079060/2549300/33111525/s65_ijraset.publication.jpg"}</script></span></span></li><li class="js-paper-rank-work_82099702 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="82099702"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 82099702, container: ".js-paper-rank-work_82099702", }); 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$(".js-view-count[data-work-id=82099702]").text(description); $(".js-view-count-work_82099702").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_82099702").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="82099702"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">4</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="1432" rel="nofollow" href="https://www.academia.edu/Documents/in/Natural_Language_Processing">Natural Language Processing</a>, <script data-card-contents-for-ri="1432" type="text/json">{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="2008" rel="nofollow" href="https://www.academia.edu/Documents/in/Machine_Learning">Machine Learning</a>, <script data-card-contents-for-ri="2008" type="text/json">{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="9246" rel="nofollow" href="https://www.academia.edu/Documents/in/Social_Media">Social Media</a>, <script data-card-contents-for-ri="9246" type="text/json">{"id":9246,"name":"Social Media","url":"https://www.academia.edu/Documents/in/Social_Media?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="12756" rel="nofollow" href="https://www.academia.edu/Documents/in/Cyberbullying">Cyberbullying</a><script data-card-contents-for-ri="12756" type="text/json">{"id":12756,"name":"Cyberbullying","url":"https://www.academia.edu/Documents/in/Cyberbullying?f_ri=1432","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=82099702]'), work: {"id":82099702,"title":"Cyberbullying Detection using Natural Language Processing","created_at":"2022-06-23T05:44:34.742-07:00","url":"https://www.academia.edu/82099702/Cyberbullying_Detection_using_Natural_Language_Processing?f_ri=1432","dom_id":"work_82099702","summary":"Around the world, the use of the Internet and social media has increased exponentially, and they have become an integral part of daily life. It allows people to share their thoughts, feelings, and ideas with their loved ones through the Internet and social media. But with social networking sites becoming more popular, cyberbullying is on the rise. Using technology as a medium to bully someone is known as Cyberbullying. The Internet can be a source of abusive and harmful content and cause harm to others. Social networking sites provide a great medium for harassment, bullies, and youngsters who use these sites are vulnerable to attacks. Bullying can have long-term effects on adolescents' ability to socialize and build lasting friendships Victims of cyberbullying often feel humiliated. social media users often can hide their identity, which helps misuse the available features. The use of offensive language has become one of the most popular issues on social networking. Text containing any form of abusive conduct that displays acts intended to hurt others is offensive language. Cyberbullying frequently leads to serious mental and physical distress, particularly for women and children, and sometimes forces them to commit suicide. The purpose of this project is to develop a technique that is effective to detect and avoid cyberbullying on social networking sites we are using Natural Language Processing and other machine learning algorithms. The dataset that we used for this project was collected from Kaggle, it contains data from Twitter that is then labeled to train the algorithm. Several classifiers are used to train and recognize bullying actions. The evaluation of the proposed Model for cyberbullying dataset shows that Logistic Regression performs better and achieves good accuracy than SVM, Ransom forest, Naive-Bayes, and Xgboost algorithm.","downloadable_attachments":[{"id":87914095,"asset_id":82099702,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":6079060,"first_name":"IJRASET","last_name":"Publication","domain_name":"independent","page_name":"IJRASETPublication","display_name":"IJRASET Publication","profile_url":"https://independent.academia.edu/IJRASETPublication?f_ri=1432","photo":"https://0.academia-photos.com/6079060/2549300/33111525/s65_ijraset.publication.jpg"}],"research_interests":[{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true},{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=1432","nofollow":true},{"id":9246,"name":"Social Media","url":"https://www.academia.edu/Documents/in/Social_Media?f_ri=1432","nofollow":true},{"id":12756,"name":"Cyberbullying","url":"https://www.academia.edu/Documents/in/Cyberbullying?f_ri=1432","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_77273779" data-work_id="77273779" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/77273779/Off_line_Nepali_handwritten_character_recognition_using_Multilayer_Perceptron_and_Radial_Basis_Function_neural_networks">Off-line Nepali handwritten character recognition using Multilayer Perceptron and Radial Basis Function neural networks</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">An off-line Nepali handwritten character recognition, based on the neural networks, is described in this paper. A good set of spatial features are extracted from character images. Accuracy and efficiency of Multilayer Perceptron (MLP) and... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_77273779" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">An off-line Nepali handwritten character recognition, based on the neural networks, is described in this paper. A good set of spatial features are extracted from character images. Accuracy and efficiency of Multilayer Perceptron (MLP) and Radial Basis Function (RBF) classifiers are analyzed. Recognition systems are tested with three datasets for Nepali handwritten numerals, vowels and consonants. The strength of this research is the efficient feature extraction and the comprehensive recognition techniques, due to which, the recognition accuracy of 94.44% is obtained for numeral dataset, 86.04% is obtained for vowel dataset and 80.25% is obtained for consonant dataset. In all cases, RBF based recognition system outperforms MLP based recognition system but RBF based recognition system takes little more time while training.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/77273779" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="a55381aa29ab2f3c194b6034431d0948" rel="nofollow" data-download="{"attachment_id":84712576,"asset_id":77273779,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/84712576/download_file?st=MTczOTgzODMzOCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="1209271" href="https://cdcsit.academia.edu/AshokPant">Ashok K Pant</a><script data-card-contents-for-user="1209271" type="text/json">{"id":1209271,"first_name":"Ashok","last_name":"Pant","domain_name":"cdcsit","page_name":"AshokPant","display_name":"Ashok K Pant","profile_url":"https://cdcsit.academia.edu/AshokPant?f_ri=1432","photo":"https://0.academia-photos.com/1209271/2846502/9732418/s65_ashok.pant.jpg"}</script></span></span></li><li class="js-paper-rank-work_77273779 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="77273779"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 77273779, container: ".js-paper-rank-work_77273779", }); });</script></li><li class="js-percentile-work_77273779 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 77273779; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_77273779"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_77273779 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="77273779"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 77273779; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=77273779]").text(description); $(".js-view-count-work_77273779").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_77273779").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="77273779"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">8</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="854" rel="nofollow" href="https://www.academia.edu/Documents/in/Computer_Vision">Computer Vision</a>, <script data-card-contents-for-ri="854" type="text/json">{"id":854,"name":"Computer Vision","url":"https://www.academia.edu/Documents/in/Computer_Vision?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="1185" rel="nofollow" href="https://www.academia.edu/Documents/in/Image_Processing">Image Processing</a>, <script data-card-contents-for-ri="1185" type="text/json">{"id":1185,"name":"Image Processing","url":"https://www.academia.edu/Documents/in/Image_Processing?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="1432" rel="nofollow" href="https://www.academia.edu/Documents/in/Natural_Language_Processing">Natural Language Processing</a>, <script data-card-contents-for-ri="1432" type="text/json">{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="2008" rel="nofollow" href="https://www.academia.edu/Documents/in/Machine_Learning">Machine Learning</a><script data-card-contents-for-ri="2008" type="text/json">{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=1432","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=77273779]'), work: {"id":77273779,"title":"Off-line Nepali handwritten character recognition using Multilayer Perceptron and Radial Basis Function neural networks","created_at":"2022-04-22T06:49:54.174-07:00","url":"https://www.academia.edu/77273779/Off_line_Nepali_handwritten_character_recognition_using_Multilayer_Perceptron_and_Radial_Basis_Function_neural_networks?f_ri=1432","dom_id":"work_77273779","summary":"An off-line Nepali handwritten character recognition, based on the neural networks, is described in this paper. A good set of spatial features are extracted from character images. Accuracy and efficiency of Multilayer Perceptron (MLP) and Radial Basis Function (RBF) classifiers are analyzed. Recognition systems are tested with three datasets for Nepali handwritten numerals, vowels and consonants. The strength of this research is the efficient feature extraction and the comprehensive recognition techniques, due to which, the recognition accuracy of 94.44% is obtained for numeral dataset, 86.04% is obtained for vowel dataset and 80.25% is obtained for consonant dataset. In all cases, RBF based recognition system outperforms MLP based recognition system but RBF based recognition system takes little more time while training.","downloadable_attachments":[{"id":84712576,"asset_id":77273779,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":1209271,"first_name":"Ashok","last_name":"Pant","domain_name":"cdcsit","page_name":"AshokPant","display_name":"Ashok K Pant","profile_url":"https://cdcsit.academia.edu/AshokPant?f_ri=1432","photo":"https://0.academia-photos.com/1209271/2846502/9732418/s65_ashok.pant.jpg"}],"research_interests":[{"id":854,"name":"Computer Vision","url":"https://www.academia.edu/Documents/in/Computer_Vision?f_ri=1432","nofollow":true},{"id":1185,"name":"Image Processing","url":"https://www.academia.edu/Documents/in/Image_Processing?f_ri=1432","nofollow":true},{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true},{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=1432","nofollow":true},{"id":29027,"name":"Handwriting Recognition (Computer Vision)","url":"https://www.academia.edu/Documents/in/Handwriting_Recognition_Computer_Vision_?f_ri=1432"},{"id":54123,"name":"Artificial Neural Networks","url":"https://www.academia.edu/Documents/in/Artificial_Neural_Networks?f_ri=1432"},{"id":56368,"name":"Image Classification","url":"https://www.academia.edu/Documents/in/Image_Classification?f_ri=1432"},{"id":160144,"name":"Feature Extraction","url":"https://www.academia.edu/Documents/in/Feature_Extraction?f_ri=1432"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_71774034" data-work_id="71774034" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/71774034/A_Note_on_Ontology_and_Ordinary_Language">A Note on Ontology and Ordinary Language</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">We argue for a compositional semantics grounded in a strongly typed ontology that reflects our commonsense view of the world and the way we talk about it. Assuming such a structure we show that the semantics of various natural language... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_71774034" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">We argue for a compositional semantics grounded in a strongly typed ontology that reflects our commonsense view of the world and the way we talk about it. Assuming such a structure we show that the semantics of various natural language phenomena may become nearly trivial.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/71774034" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="aa16e5862a5973872d83ab83bd532f89" rel="nofollow" data-download="{"attachment_id":80978689,"asset_id":71774034,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/80978689/download_file?st=MTczOTgzODMzOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="68268932" href="https://neu.academia.edu/WalidSaba">Walid Saba</a><script data-card-contents-for-user="68268932" type="text/json">{"id":68268932,"first_name":"Walid","last_name":"Saba","domain_name":"neu","page_name":"WalidSaba","display_name":"Walid Saba","profile_url":"https://neu.academia.edu/WalidSaba?f_ri=1432","photo":"https://0.academia-photos.com/68268932/18804045/155552639/s65_walid.saba.jpg"}</script></span></span></li><li class="js-paper-rank-work_71774034 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="71774034"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 71774034, container: ".js-paper-rank-work_71774034", }); 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Assuming such a structure we show that the semantics of various natural language phenomena may become nearly trivial.","downloadable_attachments":[{"id":80978689,"asset_id":71774034,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":68268932,"first_name":"Walid","last_name":"Saba","domain_name":"neu","page_name":"WalidSaba","display_name":"Walid Saba","profile_url":"https://neu.academia.edu/WalidSaba?f_ri=1432","photo":"https://0.academia-photos.com/68268932/18804045/155552639/s65_walid.saba.jpg"}],"research_interests":[{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence?f_ri=1432","nofollow":true},{"id":804,"name":"Metaphysics","url":"https://www.academia.edu/Documents/in/Metaphysics?f_ri=1432","nofollow":true},{"id":806,"name":"Philosophy of Mind","url":"https://www.academia.edu/Documents/in/Philosophy_of_Mind?f_ri=1432","nofollow":true},{"id":807,"name":"Philosophy Of Language","url":"https://www.academia.edu/Documents/in/Philosophy_Of_Language?f_ri=1432","nofollow":true},{"id":924,"name":"Logic","url":"https://www.academia.edu/Documents/in/Logic?f_ri=1432"},{"id":1200,"name":"Languages and Linguistics","url":"https://www.academia.edu/Documents/in/Languages_and_Linguistics?f_ri=1432"},{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432"},{"id":2238,"name":"Pragmatics","url":"https://www.academia.edu/Documents/in/Pragmatics?f_ri=1432"},{"id":2349,"name":"Semantics","url":"https://www.academia.edu/Documents/in/Semantics?f_ri=1432"},{"id":3268,"name":"Computational Linguistics","url":"https://www.academia.edu/Documents/in/Computational_Linguistics?f_ri=1432"},{"id":18174,"name":"Language","url":"https://www.academia.edu/Documents/in/Language?f_ri=1432"},{"id":42799,"name":"Speech","url":"https://www.academia.edu/Documents/in/Speech?f_ri=1432"},{"id":97618,"name":"Natural language","url":"https://www.academia.edu/Documents/in/Natural_language?f_ri=1432"},{"id":287095,"name":"Knowledge Representation and Reasoning","url":"https://www.academia.edu/Documents/in/Knowledge_Representation_and_Reasoning?f_ri=1432"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_69385450" data-work_id="69385450" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/69385450/Portuguese_Large_scale_Language_Resources_for_NLP_Applications">Portuguese Large-scale Language Resources for NLP Applications</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The paper describes Portuguese large-scale linguistic resources, mainly computational lexicons and grammars, developed by LabEL. These resources are formalized and applied to texts by means of finite-state techniques, more and more... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_69385450" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The paper describes Portuguese large-scale linguistic resources, mainly computational lexicons and grammars, developed by LabEL. These resources are formalized and applied to texts by means of finite-state techniques, more and more acknowledged in Natural Language Processing. On the one hand, it illustrates methods on lexical representation for simple words and multi-word expressions; on the other hand, it provides examples (in form of concordances) of linguistic structures recognized after the application of disambiguation and parsing grammars to texts. The paper ends with a short reference to the publicly available data highlighting its contribution towards dissemination of LabEL's knowledge on language technology.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/69385450" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="c70d470d9946d8fe44d945a93fe17024" rel="nofollow" data-download="{"attachment_id":79503591,"asset_id":69385450,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/79503591/download_file?st=MTczOTgzODMzOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="97637956" href="https://independent.academia.edu/PaulaCarvalho76">Paula Carvalho</a><script data-card-contents-for-user="97637956" type="text/json">{"id":97637956,"first_name":"Paula","last_name":"Carvalho","domain_name":"independent","page_name":"PaulaCarvalho76","display_name":"Paula Carvalho","profile_url":"https://independent.academia.edu/PaulaCarvalho76?f_ri=1432","photo":"https://0.academia-photos.com/97637956/173261947/163273541/s65_paula.carvalho.jpeg"}</script></span></span></li><li class="js-paper-rank-work_69385450 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="69385450"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 69385450, container: ".js-paper-rank-work_69385450", }); 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These resources are formalized and applied to texts by means of finite-state techniques, more and more acknowledged in Natural Language Processing. On the one hand, it illustrates methods on lexical representation for simple words and multi-word expressions; on the other hand, it provides examples (in form of concordances) of linguistic structures recognized after the application of disambiguation and parsing grammars to texts. The paper ends with a short reference to the publicly available data highlighting its contribution towards dissemination of LabEL's knowledge on language technology.","downloadable_attachments":[{"id":79503591,"asset_id":69385450,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":97637956,"first_name":"Paula","last_name":"Carvalho","domain_name":"independent","page_name":"PaulaCarvalho76","display_name":"Paula Carvalho","profile_url":"https://independent.academia.edu/PaulaCarvalho76?f_ri=1432","photo":"https://0.academia-photos.com/97637956/173261947/163273541/s65_paula.carvalho.jpeg"}],"research_interests":[{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true},{"id":40276,"name":"Proceedings","url":"https://www.academia.edu/Documents/in/Proceedings?f_ri=1432","nofollow":true},{"id":55199,"name":"Language Resources","url":"https://www.academia.edu/Documents/in/Language_Resources?f_ri=1432","nofollow":true},{"id":413645,"name":"Portuguese Language Processing","url":"https://www.academia.edu/Documents/in/Portuguese_Language_Processing?f_ri=1432","nofollow":true},{"id":758278,"name":"Large Scale","url":"https://www.academia.edu/Documents/in/Large_Scale?f_ri=1432"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_67525032" data-work_id="67525032" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" rel="nofollow" href="https://www.academia.edu/67525032/Named_entity_recognition_in_Vietnamese_using_classifier_voting">Named entity recognition in Vietnamese using classifier voting</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Named entity recognition (NER) is one of the fundamental tasks in natural-language processing (NLP). Though the combination of different classifiers has been widely applied in several well-studied languages, this is the first time this... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_67525032" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Named entity recognition (NER) is one of the fundamental tasks in natural-language processing (NLP). Though the combination of different classifiers has been widely applied in several well-studied languages, this is the first time this method has been applied to Vietnamese. In this article, we describe how voting techniques can improve the performance of Vietnamese NER. By combining several state-of-the-art machine-learning algorithms using voting strategies, our final result outperforms individual algorithms and gained an F-measure of 89.12. A detailed discussion about the challenges of NER in Vietnamese is also presented.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/67525032" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="11053660" href="https://independent.academia.edu/tminhtri">tran minh tri</a><script data-card-contents-for-user="11053660" type="text/json">{"id":11053660,"first_name":"tran","last_name":"minh tri","domain_name":"independent","page_name":"tminhtri","display_name":"tran minh tri","profile_url":"https://independent.academia.edu/tminhtri?f_ri=1432","photo":"https://0.academia-photos.com/11053660/24186220/23140951/s65_tran.minh_tri.jpg"}</script></span></span></li><li class="js-paper-rank-work_67525032 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="67525032"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 67525032, container: ".js-paper-rank-work_67525032", }); });</script></li><li class="js-percentile-work_67525032 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 67525032; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_67525032"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_67525032 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="67525032"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 67525032; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=67525032]").text(description); $(".js-view-count-work_67525032").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_67525032").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="67525032"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">11</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="37" rel="nofollow" href="https://www.academia.edu/Documents/in/Information_Systems">Information Systems</a>, <script data-card-contents-for-ri="37" type="text/json">{"id":37,"name":"Information Systems","url":"https://www.academia.edu/Documents/in/Information_Systems?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="1432" rel="nofollow" href="https://www.academia.edu/Documents/in/Natural_Language_Processing">Natural Language Processing</a>, <script data-card-contents-for-ri="1432" type="text/json">{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="2008" rel="nofollow" href="https://www.academia.edu/Documents/in/Machine_Learning">Machine Learning</a>, <script data-card-contents-for-ri="2008" type="text/json">{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="10408" rel="nofollow" href="https://www.academia.edu/Documents/in/Support_Vector_Machines">Support Vector Machines</a><script data-card-contents-for-ri="10408" type="text/json">{"id":10408,"name":"Support Vector Machines","url":"https://www.academia.edu/Documents/in/Support_Vector_Machines?f_ri=1432","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=67525032]'), work: {"id":67525032,"title":"Named entity recognition in Vietnamese using classifier voting","created_at":"2022-01-07T06:16:35.167-08:00","url":"https://www.academia.edu/67525032/Named_entity_recognition_in_Vietnamese_using_classifier_voting?f_ri=1432","dom_id":"work_67525032","summary":"Named entity recognition (NER) is one of the fundamental tasks in natural-language processing (NLP). Though the combination of different classifiers has been widely applied in several well-studied languages, this is the first time this method has been applied to Vietnamese. In this article, we describe how voting techniques can improve the performance of Vietnamese NER. By combining several state-of-the-art machine-learning algorithms using voting strategies, our final result outperforms individual algorithms and gained an F-measure of 89.12. A detailed discussion about the challenges of NER in Vietnamese is also presented.","downloadable_attachments":[],"ordered_authors":[{"id":11053660,"first_name":"tran","last_name":"minh tri","domain_name":"independent","page_name":"tminhtri","display_name":"tran minh tri","profile_url":"https://independent.academia.edu/tminhtri?f_ri=1432","photo":"https://0.academia-photos.com/11053660/24186220/23140951/s65_tran.minh_tri.jpg"}],"research_interests":[{"id":37,"name":"Information Systems","url":"https://www.academia.edu/Documents/in/Information_Systems?f_ri=1432","nofollow":true},{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true},{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=1432","nofollow":true},{"id":10408,"name":"Support Vector Machines","url":"https://www.academia.edu/Documents/in/Support_Vector_Machines?f_ri=1432","nofollow":true},{"id":16135,"name":"Conditional Random Fields","url":"https://www.academia.edu/Documents/in/Conditional_Random_Fields?f_ri=1432"},{"id":29205,"name":"Named Entity Recognition","url":"https://www.academia.edu/Documents/in/Named_Entity_Recognition?f_ri=1432"},{"id":39401,"name":"Vietnamese","url":"https://www.academia.edu/Documents/in/Vietnamese?f_ri=1432"},{"id":191289,"name":"Support vector machine","url":"https://www.academia.edu/Documents/in/Support_vector_machine?f_ri=1432"},{"id":345767,"name":"Naive Bayes","url":"https://www.academia.edu/Documents/in/Naive_Bayes?f_ri=1432"},{"id":688444,"name":"Conditional Random Field","url":"https://www.academia.edu/Documents/in/Conditional_Random_Field?f_ri=1432"},{"id":2070034,"name":"Data Format","url":"https://www.academia.edu/Documents/in/Data_Format?f_ri=1432"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_66904300" data-work_id="66904300" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/66904300/Power_of_expression_in_the_electronic_patient_record_structured_data_or_narrative_text">Power of expression in the electronic patient record: structured data or narrative text?</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">This paper presents the authors' experience with the development and use of a document-centered electronic patient record (EPR) in a large teaching hospital. The development of the document-centered EPR began with the formulation of a set... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_66904300" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">This paper presents the authors' experience with the development and use of a document-centered electronic patient record (EPR) in a large teaching hospital. The development of the document-centered EPR began with the formulation of a set of critical hypotheses to facilitate both the continuation of the best medical practice and the implementation and use of the EPR. An alternate and more conventional approach-the data-centered EPR-is compared with the document-centered EPR. Various benefits and pitfalls are discussed. Finally, the choice was to offer both solutions in a tightly linked system. The need for an EPR which combines the document and data centered approaches is a reflection of the more general discussion of what the medical record will be in the future. All too often, the need for structured data conflicts with the need for free texts and the power of expression. It is not easy to evaluate the consequences of this initial decision. However, changing the foundations of the EPR after its implementation is difficult and expensive. Therefore, the selection of the correct orientation in a given hospital requires a broad-based discussion.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/66904300" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="6018e020ab33debbe289623069aa541d" rel="nofollow" data-download="{"attachment_id":77922744,"asset_id":66904300,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/77922744/download_file?st=MTczOTgzODMzOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="68504985" href="https://independent.academia.edu/PierrePlanche">Pierre Planche</a><script data-card-contents-for-user="68504985" type="text/json">{"id":68504985,"first_name":"Pierre","last_name":"Planche","domain_name":"independent","page_name":"PierrePlanche","display_name":"Pierre Planche","profile_url":"https://independent.academia.edu/PierrePlanche?f_ri=1432","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_66904300 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="66904300"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 66904300, container: ".js-paper-rank-work_66904300", }); });</script></li><li class="js-percentile-work_66904300 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 66904300; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_66904300"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_66904300 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="66904300"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 66904300; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=66904300]").text(description); $(".js-view-count-work_66904300").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_66904300").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="66904300"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">18</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="48" rel="nofollow" href="https://www.academia.edu/Documents/in/Engineering">Engineering</a>, <script data-card-contents-for-ri="48" type="text/json">{"id":48,"name":"Engineering","url":"https://www.academia.edu/Documents/in/Engineering?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="1131" rel="nofollow" href="https://www.academia.edu/Documents/in/Biomedical_Engineering">Biomedical Engineering</a>, <script data-card-contents-for-ri="1131" type="text/json">{"id":1131,"name":"Biomedical Engineering","url":"https://www.academia.edu/Documents/in/Biomedical_Engineering?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="1212" rel="nofollow" href="https://www.academia.edu/Documents/in/Medical_Informatics">Medical Informatics</a>, <script data-card-contents-for-ri="1212" type="text/json">{"id":1212,"name":"Medical Informatics","url":"https://www.academia.edu/Documents/in/Medical_Informatics?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="1432" rel="nofollow" href="https://www.academia.edu/Documents/in/Natural_Language_Processing">Natural Language Processing</a><script data-card-contents-for-ri="1432" type="text/json">{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=66904300]'), work: {"id":66904300,"title":"Power of expression in the electronic patient record: structured data or narrative text?","created_at":"2022-01-02T22:59:35.865-08:00","url":"https://www.academia.edu/66904300/Power_of_expression_in_the_electronic_patient_record_structured_data_or_narrative_text?f_ri=1432","dom_id":"work_66904300","summary":"This paper presents the authors' experience with the development and use of a document-centered electronic patient record (EPR) in a large teaching hospital. The development of the document-centered EPR began with the formulation of a set of critical hypotheses to facilitate both the continuation of the best medical practice and the implementation and use of the EPR. An alternate and more conventional approach-the data-centered EPR-is compared with the document-centered EPR. Various benefits and pitfalls are discussed. Finally, the choice was to offer both solutions in a tightly linked system. The need for an EPR which combines the document and data centered approaches is a reflection of the more general discussion of what the medical record will be in the future. All too often, the need for structured data conflicts with the need for free texts and the power of expression. It is not easy to evaluate the consequences of this initial decision. However, changing the foundations of the EPR after its implementation is difficult and expensive. Therefore, the selection of the correct orientation in a given hospital requires a broad-based discussion.","downloadable_attachments":[{"id":77922744,"asset_id":66904300,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":68504985,"first_name":"Pierre","last_name":"Planche","domain_name":"independent","page_name":"PierrePlanche","display_name":"Pierre Planche","profile_url":"https://independent.academia.edu/PierrePlanche?f_ri=1432","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":48,"name":"Engineering","url":"https://www.academia.edu/Documents/in/Engineering?f_ri=1432","nofollow":true},{"id":1131,"name":"Biomedical Engineering","url":"https://www.academia.edu/Documents/in/Biomedical_Engineering?f_ri=1432","nofollow":true},{"id":1212,"name":"Medical Informatics","url":"https://www.academia.edu/Documents/in/Medical_Informatics?f_ri=1432","nofollow":true},{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true},{"id":2349,"name":"Semantics","url":"https://www.academia.edu/Documents/in/Semantics?f_ri=1432"},{"id":2852,"name":"Narrative","url":"https://www.academia.edu/Documents/in/Narrative?f_ri=1432"},{"id":8910,"name":"Evaluation","url":"https://www.academia.edu/Documents/in/Evaluation?f_ri=1432"},{"id":23890,"name":"Comparative Study","url":"https://www.academia.edu/Documents/in/Comparative_Study?f_ri=1432"},{"id":63288,"name":"Narration","url":"https://www.academia.edu/Documents/in/Narration?f_ri=1432"},{"id":95074,"name":"Structured data","url":"https://www.academia.edu/Documents/in/Structured_data?f_ri=1432"},{"id":108782,"name":"Data","url":"https://www.academia.edu/Documents/in/Data?f_ri=1432"},{"id":216941,"name":"Data Center","url":"https://www.academia.edu/Documents/in/Data_Center?f_ri=1432"},{"id":224276,"name":"Feasibility","url":"https://www.academia.edu/Documents/in/Feasibility?f_ri=1432"},{"id":238655,"name":"Implementation","url":"https://www.academia.edu/Documents/in/Implementation?f_ri=1432"},{"id":874382,"name":"Medical Records","url":"https://www.academia.edu/Documents/in/Medical_Records?f_ri=1432"},{"id":1106271,"name":"Medical Record","url":"https://www.academia.edu/Documents/in/Medical_Record?f_ri=1432"},{"id":2213585,"name":"Information System","url":"https://www.academia.edu/Documents/in/Information_System?f_ri=1432"},{"id":3763225,"name":"Medical and Health Sciences","url":"https://www.academia.edu/Documents/in/Medical_and_Health_Sciences?f_ri=1432"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_34467255" data-work_id="34467255" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" rel="nofollow" href="https://www.academia.edu/34467255/COHESIVE_MULTI_ORIENTED_TEXT_DETECTION_AND_RECOGNITION_STRUCTURE_IN_NATURAL_SCENE_IMAGES_REGIONS_HAS_EXPOSED">COHESIVE MULTI-ORIENTED TEXT DETECTION AND RECOGNITION STRUCTURE IN NATURAL SCENE IMAGES REGIONS HAS EXPOSED</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Scene text recognition brings various new challenges occurs in recent years. Detecting and recognizing text in scenes entails some of the equivalent problems as document processing, but there are also numerous novel problems to face for... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_34467255" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Scene text recognition brings various new challenges occurs in recent years. Detecting and recognizing text in scenes entails some of the equivalent problems as document processing, but there are also numerous novel problems to face for recognizing text in natural scene images. Recent research in these regions has exposed several promise but present is motionless much effort to be entire in these regions. Most existing techniques have focused on detecting horizontal or near-horizontal texts. In this paper, we propose a new scheme which detects texts of arbitrary directions in natural scene images. Our algorithm is equipped with two sets of characteristics specially designed for capturing both the natural characteristics of texts using MSER regions using Otsu method. To better estimate our algorithm and compare it with other existing algorithms, we are using existing MSRA Dataset, ICDAR Dataset, and our new dataset, which includes various texts in various real-world situations. Experiments results on these standard datasets and the proposed dataset shows that our algorithm compares positively with the modern algorithms when using horizontal texts and accomplishes significantly improved performance on texts of random orientations in composite natural scenes images.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/34467255" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="063be34d3b9ad155d35eca36c7df4f0d" rel="nofollow" data-download="{"attachment_id":54339612,"asset_id":34467255,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/54339612/download_file?st=MTczOTgzODMzOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="15995294" rel="nofollow" href="https://independent.academia.edu/IJDPSJournal">Call for paper-International Journal of Distributed and Parallel Systems (IJDPS)</a><script data-card-contents-for-user="15995294" type="text/json">{"id":15995294,"first_name":"Call for paper-International Journal of Distributed and Parallel Systems","last_name":"(IJDPS)","domain_name":"independent","page_name":"IJDPSJournal","display_name":"Call for paper-International Journal of Distributed and Parallel Systems (IJDPS)","profile_url":"https://independent.academia.edu/IJDPSJournal?f_ri=1432","photo":"https://0.academia-photos.com/15995294/9436000/48008008/s65_international_journal_of_distributed_and_parallel_systems._ijdps_.png"}</script></span></span></li><li class="js-paper-rank-work_34467255 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="34467255"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 34467255, container: ".js-paper-rank-work_34467255", }); 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Detecting and recognizing text in scenes entails some of the equivalent problems as document processing, but there are also numerous novel problems to face for recognizing text in natural scene images. Recent research in these regions has exposed several promise but present is motionless much effort to be entire in these regions. Most existing techniques have focused on detecting horizontal or near-horizontal texts. In this paper, we propose a new scheme which detects texts of arbitrary directions in natural scene images. Our algorithm is equipped with two sets of characteristics specially designed for capturing both the natural characteristics of texts using MSER regions using Otsu method. To better estimate our algorithm and compare it with other existing algorithms, we are using existing MSRA Dataset, ICDAR Dataset, and our new dataset, which includes various texts in various real-world situations. Experiments results on these standard datasets and the proposed dataset shows that our algorithm compares positively with the modern algorithms when using horizontal texts and accomplishes significantly improved performance on texts of random orientations in composite natural scenes images.","downloadable_attachments":[{"id":54339612,"asset_id":34467255,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":15995294,"first_name":"Call for paper-International Journal of Distributed and Parallel Systems","last_name":"(IJDPS)","domain_name":"independent","page_name":"IJDPSJournal","display_name":"Call for paper-International Journal of Distributed and Parallel Systems (IJDPS)","profile_url":"https://independent.academia.edu/IJDPSJournal?f_ri=1432","photo":"https://0.academia-photos.com/15995294/9436000/48008008/s65_international_journal_of_distributed_and_parallel_systems._ijdps_.png"}],"research_interests":[{"id":428,"name":"Algorithms","url":"https://www.academia.edu/Documents/in/Algorithms?f_ri=1432","nofollow":true},{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true},{"id":5109,"name":"Pattern Recognition","url":"https://www.academia.edu/Documents/in/Pattern_Recognition?f_ri=1432","nofollow":true},{"id":21949,"name":"Horizontal Gene Transfer","url":"https://www.academia.edu/Documents/in/Horizontal_Gene_Transfer?f_ri=1432","nofollow":true},{"id":23214,"name":"Documentary Film","url":"https://www.academia.edu/Documents/in/Documentary_Film?f_ri=1432"},{"id":61234,"name":"Stroke","url":"https://www.academia.edu/Documents/in/Stroke?f_ri=1432"},{"id":386000,"name":"Text Detection in Natural Scene","url":"https://www.academia.edu/Documents/in/Text_Detection_in_Natural_Scene?f_ri=1432"},{"id":803119,"name":"Researchers","url":"https://www.academia.edu/Documents/in/Researchers?f_ri=1432"},{"id":955461,"name":"Text Extraction","url":"https://www.academia.edu/Documents/in/Text_Extraction?f_ri=1432"},{"id":961848,"name":"Paragraph","url":"https://www.academia.edu/Documents/in/Paragraph?f_ri=1432"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_33670562 coauthored" data-work_id="33670562" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest 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href="https://www.academia.edu/Documents/in/Kartvelian_Languages">Kartvelian Languages</a><script data-card-contents-for-ri="97186" type="text/json">{"id":97186,"name":"Kartvelian Languages","url":"https://www.academia.edu/Documents/in/Kartvelian_Languages?f_ri=1432","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=33670562]'), work: {"id":33670562,"title":"GREGORY of Nazianzus, Oratio XI (Georgian Concordance - Euthymius and Ephrem), https://uclouvain.be/fr/instituts-recherche/incal/ciol/the-concordances-of-the-gregori-project.html","created_at":"2017-06-27T04:36:30.845-07:00","url":"https://www.academia.edu/33670562/GREGORY_of_Nazianzus_Oratio_XI_Georgian_Concordance_Euthymius_and_Ephrem_https_uclouvain_be_fr_instituts_recherche_incal_ciol_the_concordances_of_the_gregori_project_html?f_ri=1432","dom_id":"work_33670562","summary":null,"downloadable_attachments":[{"id":53688895,"asset_id":33670562,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":1077366,"first_name":"PATARIDZE","last_name":"Tamara","domain_name":"uclouvain","page_name":"PATARIDZETamara","display_name":"PATARIDZE Tamara","profile_url":"https://uclouvain.academia.edu/PATARIDZETamara?f_ri=1432","photo":"https://0.academia-photos.com/1077366/439269/29946480/s65_pataridze.tamara.jpg"},{"id":899075,"first_name":"Bastien","last_name":"Kindt","domain_name":"uclouvain","page_name":"BastienKindt","display_name":"Bastien Kindt","profile_url":"https://uclouvain.academia.edu/BastienKindt?f_ri=1432","photo":"https://0.academia-photos.com/899075/330539/113640380/s65_bastien.kindt.jpg"}],"research_interests":[{"id":1197,"name":"Digital Humanities","url":"https://www.academia.edu/Documents/in/Digital_Humanities?f_ri=1432","nofollow":true},{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true},{"id":97186,"name":"Kartvelian Languages","url":"https://www.academia.edu/Documents/in/Kartvelian_Languages?f_ri=1432","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_377311" data-work_id="377311" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/377311/Linguistic_Facts_As_Predicates_Over_Ranges_of_the_Sentence">Linguistic Facts As Predicates Over Ranges of the Sentence</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/377311" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="9504acecf29eaedf5513cf40e943a3ab" rel="nofollow" data-download="{"attachment_id":1909899,"asset_id":377311,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button 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Sentence","created_at":"2010-11-26T18:26:35.795-08:00","url":"https://www.academia.edu/377311/Linguistic_Facts_As_Predicates_Over_Ranges_of_the_Sentence?f_ri=1432","dom_id":"work_377311","summary":null,"downloadable_attachments":[{"id":1909899,"asset_id":377311,"asset_type":"Work","always_allow_download":false},{"id":36301158,"asset_id":377311,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":287875,"first_name":"Benoît","last_name":"Sagot","domain_name":"inrialpes","page_name":"BenoîtSagot","display_name":"Benoît Sagot","profile_url":"https://inrialpes.academia.edu/Beno%C3%AEtSagot?f_ri=1432","photo":"https://0.academia-photos.com/287875/70926/15593916/s65_beno_t.sagot.jpg"}],"research_interests":[{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true},{"id":33890,"name":"Language Processing","url":"https://www.academia.edu/Documents/in/Language_Processing?f_ri=1432","nofollow":true},{"id":1697739,"name":"Dependence Graph","url":"https://www.academia.edu/Documents/in/Dependence_Graph?f_ri=1432","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_70892251" data-work_id="70892251" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/70892251/Baselines_for_Natural_Language_Processing_Tasks_Based_on_Soft_Cardinality_Spectra">Baselines for Natural Language Processing Tasks Based on Soft Cardinality Spectra</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Abstract. Soft-cardinality spectra (SC spectra) is a new method of approximation for text strings in linear time, which divides text strings into character q-grams of different sizes. The method allows simultaneous use of weighting at... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_70892251" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Abstract. Soft-cardinality spectra (SC spectra) is a new method of approximation for text strings in linear time, which divides text strings into character q-grams of different sizes. The method allows simultaneous use of weighting at term and q-gram levels. SC spectra in combination with resemblance coefficients allows the construction of a family of text similarity functions that only use the surface information of the texts and weights obtained in the same text collection. These similarity measures can be used in various tasks of natural ...</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/70892251" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="8c9c81290d30cdabb5fd11f4a4fbb57e" rel="nofollow" data-download="{"attachment_id":80453120,"asset_id":70892251,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/80453120/download_file?st=MTczOTgzODMzOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="3184141" href="https://independent.academia.edu/SergioJimenez1">Sergio Jimenez</a><script data-card-contents-for-user="3184141" type="text/json">{"id":3184141,"first_name":"Sergio","last_name":"Jimenez","domain_name":"independent","page_name":"SergioJimenez1","display_name":"Sergio Jimenez","profile_url":"https://independent.academia.edu/SergioJimenez1?f_ri=1432","photo":"https://0.academia-photos.com/3184141/20115028/19868263/s65_sergio.jimenez.jpg"}</script></span></span></li><li class="js-paper-rank-work_70892251 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="70892251"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 70892251, container: ".js-paper-rank-work_70892251", }); 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Soft-cardinality spectra (SC spectra) is a new method of approximation for text strings in linear time, which divides text strings into character q-grams of different sizes. The method allows simultaneous use of weighting at term and q-gram levels. SC spectra in combination with resemblance coefficients allows the construction of a family of text similarity functions that only use the surface information of the texts and weights obtained in the same text collection. These similarity measures can be used in various tasks of natural ...","downloadable_attachments":[{"id":80453120,"asset_id":70892251,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":3184141,"first_name":"Sergio","last_name":"Jimenez","domain_name":"independent","page_name":"SergioJimenez1","display_name":"Sergio Jimenez","profile_url":"https://independent.academia.edu/SergioJimenez1?f_ri=1432","photo":"https://0.academia-photos.com/3184141/20115028/19868263/s65_sergio.jimenez.jpg"}],"research_interests":[{"id":464,"name":"Information Retrieval","url":"https://www.academia.edu/Documents/in/Information_Retrieval?f_ri=1432","nofollow":true},{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true},{"id":125863,"name":"Applied Mathematics and Computational Science","url":"https://www.academia.edu/Documents/in/Applied_Mathematics_and_Computational_Science?f_ri=1432","nofollow":true},{"id":316811,"name":"Text Similarity","url":"https://www.academia.edu/Documents/in/Text_Similarity?f_ri=1432","nofollow":true},{"id":320143,"name":"Soft Cardinality","url":"https://www.academia.edu/Documents/in/Soft_Cardinality?f_ri=1432"},{"id":475215,"name":"Text Comparison","url":"https://www.academia.edu/Documents/in/Text_Comparison?f_ri=1432"},{"id":1029477,"name":"Linear Time","url":"https://www.academia.edu/Documents/in/Linear_Time?f_ri=1432"},{"id":1484491,"name":"Similarity Function","url":"https://www.academia.edu/Documents/in/Similarity_Function?f_ri=1432"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_49428981" data-work_id="49428981" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/49428981/Contextual_natural_language_processing_and_DAML_for_understanding_software_requirements_specifications">Contextual natural language processing and DAML for understanding software requirements specifications</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/49428981" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="f2139b446848b7e4916c58772abe2306" rel="nofollow" data-download="{"attachment_id":67782015,"asset_id":49428981,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/67782015/download_file?st=MTczOTgzODMzOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="6690185" href="https://txstate.academia.edu/BarrettBryant">Barrett Bryant</a><script data-card-contents-for-user="6690185" type="text/json">{"id":6690185,"first_name":"Barrett","last_name":"Bryant","domain_name":"txstate","page_name":"BarrettBryant","display_name":"Barrett Bryant","profile_url":"https://txstate.academia.edu/BarrettBryant?f_ri=1432","photo":"https://0.academia-photos.com/6690185/2636268/121825035/s65_barrett.bryant.jpg"}</script></span></span></li><li class="js-paper-rank-work_49428981 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="49428981"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 49428981, container: ".js-paper-rank-work_49428981", }); 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$(".js-view-count[data-work-id=49428981]").text(description); $(".js-view-count-work_49428981").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_49428981").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="49428981"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">6</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="449" rel="nofollow" href="https://www.academia.edu/Documents/in/Software_Engineering">Software Engineering</a>, <script data-card-contents-for-ri="449" type="text/json">{"id":449,"name":"Software Engineering","url":"https://www.academia.edu/Documents/in/Software_Engineering?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="1432" rel="nofollow" href="https://www.academia.edu/Documents/in/Natural_Language_Processing">Natural Language Processing</a>, <script data-card-contents-for-ri="1432" type="text/json">{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="96628" rel="nofollow" href="https://www.academia.edu/Documents/in/Formal_Specification">Formal Specification</a>, <script data-card-contents-for-ri="96628" type="text/json">{"id":96628,"name":"Formal Specification","url":"https://www.academia.edu/Documents/in/Formal_Specification?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="97618" rel="nofollow" href="https://www.academia.edu/Documents/in/Natural_language">Natural language</a><script data-card-contents-for-ri="97618" type="text/json">{"id":97618,"name":"Natural language","url":"https://www.academia.edu/Documents/in/Natural_language?f_ri=1432","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=49428981]'), work: {"id":49428981,"title":"Contextual natural language processing and DAML for understanding software requirements specifications","created_at":"2021-06-27T20:37:25.009-07:00","url":"https://www.academia.edu/49428981/Contextual_natural_language_processing_and_DAML_for_understanding_software_requirements_specifications?f_ri=1432","dom_id":"work_49428981","summary":null,"downloadable_attachments":[{"id":67782015,"asset_id":49428981,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":6690185,"first_name":"Barrett","last_name":"Bryant","domain_name":"txstate","page_name":"BarrettBryant","display_name":"Barrett Bryant","profile_url":"https://txstate.academia.edu/BarrettBryant?f_ri=1432","photo":"https://0.academia-photos.com/6690185/2636268/121825035/s65_barrett.bryant.jpg"}],"research_interests":[{"id":449,"name":"Software Engineering","url":"https://www.academia.edu/Documents/in/Software_Engineering?f_ri=1432","nofollow":true},{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true},{"id":96628,"name":"Formal Specification","url":"https://www.academia.edu/Documents/in/Formal_Specification?f_ri=1432","nofollow":true},{"id":97618,"name":"Natural language","url":"https://www.academia.edu/Documents/in/Natural_language?f_ri=1432","nofollow":true},{"id":197861,"name":"Domain Knowledge","url":"https://www.academia.edu/Documents/in/Domain_Knowledge?f_ri=1432"},{"id":614639,"name":"Software Requirements Specification","url":"https://www.academia.edu/Documents/in/Software_Requirements_Specification?f_ri=1432"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_78805841 coauthored" data-work_id="78805841" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/78805841/Transductive_Visual_Verb_Sense_Disambiguation">Transductive Visual Verb Sense Disambiguation</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Verb Sense Disambiguation is a well-known task in NLP, the aim is to find the correct sense of a verb in a sentence. Recently, this problem has been extended in a multimodal scenario, by exploiting both textual and visual features of... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_78805841" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Verb Sense Disambiguation is a well-known task in NLP, the aim is to find the correct sense of a verb in a sentence. Recently, this problem has been extended in a multimodal scenario, by exploiting both textual and visual features of ambiguous verbs leading to a new problem, the Visual Verb Sense Disambiguation (VVSD). Here, the sense of a verb is assigned considering the content of an image paired with it rather than a sentence in which the verb appears. Annotating a dataset for this task is more complex than textual disambiguation, because assigning the correct sense to a pair of <image, verb> requires both non-trivial linguistic and visual skills. In this work, differently from the literature, the VVSD task will be performed in a transductive semi-supervised learning (SSL) setting, in which only a small amount of labeled information is required, reducing tremendously the need for annotated data. The disambiguation process is based on a graph-based label propagation method which takes into account mono or multimodal representations for <image, verb> pairs. Experiments have been carried out on the recently published dataset VerSe, the only available dataset for this task. The achieved results outperform the current state-of-the-art by a large margin while using only a small fraction of labeled samples per sense 1 .</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/78805841" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="5a0418bc32c23e95b18238ea89beb8ae" rel="nofollow" data-download="{"attachment_id":85721067,"asset_id":78805841,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/85721067/download_file?st=MTczOTgzODMzOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="224373587" href="https://independent.academia.edu/GianlucaBIGAGLIA">Gianluca BIGAGLIA</a><script data-card-contents-for-user="224373587" type="text/json">{"id":224373587,"first_name":"Gianluca","last_name":"BIGAGLIA","domain_name":"independent","page_name":"GianlucaBIGAGLIA","display_name":"Gianluca BIGAGLIA","profile_url":"https://independent.academia.edu/GianlucaBIGAGLIA?f_ri=1432","photo":"https://0.academia-photos.com/224373587/81693600/70289480/s65_gianluca.bigaglia.png"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-78805841">+1</span><div class="hidden js-additional-users-78805841"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://independent.academia.edu/GiudiceLorenzo">Lorenzo Giudice</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-78805841'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-78805841').html(); 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Recently, this problem has been extended in a multimodal scenario, by exploiting both textual and visual features of ambiguous verbs leading to a new problem, the Visual Verb Sense Disambiguation (VVSD). Here, the sense of a verb is assigned considering the content of an image paired with it rather than a sentence in which the verb appears. Annotating a dataset for this task is more complex than textual disambiguation, because assigning the correct sense to a pair of \u003cimage, verb\u003e requires both non-trivial linguistic and visual skills. In this work, differently from the literature, the VVSD task will be performed in a transductive semi-supervised learning (SSL) setting, in which only a small amount of labeled information is required, reducing tremendously the need for annotated data. The disambiguation process is based on a graph-based label propagation method which takes into account mono or multimodal representations for \u003cimage, verb\u003e pairs. Experiments have been carried out on the recently published dataset VerSe, the only available dataset for this task. The achieved results outperform the current state-of-the-art by a large margin while using only a small fraction of labeled samples per sense 1 .","downloadable_attachments":[{"id":85721067,"asset_id":78805841,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":224373587,"first_name":"Gianluca","last_name":"BIGAGLIA","domain_name":"independent","page_name":"GianlucaBIGAGLIA","display_name":"Gianluca BIGAGLIA","profile_url":"https://independent.academia.edu/GianlucaBIGAGLIA?f_ri=1432","photo":"https://0.academia-photos.com/224373587/81693600/70289480/s65_gianluca.bigaglia.png"},{"id":155478351,"first_name":"Lorenzo","last_name":"Giudice","domain_name":"independent","page_name":"GiudiceLorenzo","display_name":"Lorenzo Giudice","profile_url":"https://independent.academia.edu/GiudiceLorenzo?f_ri=1432","photo":"https://gravatar.com/avatar/6664762529a0ad73a19155472ae7053f?s=65"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=1432","nofollow":true},{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence?f_ri=1432","nofollow":true},{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_76515331" data-work_id="76515331" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/76515331/Biomedical_Natural_Language_Processing">Biomedical Natural Language Processing</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest">We are very grateful to Matthew Simpson and Ivo Georgiev for their thorough and thoughtful reviews of the book.</div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/76515331" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="c83b37bd8c7d2e7d0a0b778a692d63ab" rel="nofollow" data-download="{"attachment_id":84200529,"asset_id":76515331,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/84200529/download_file?st=MTczOTgzODMzOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="3430318" href="https://ucdenver.academia.edu/KevinBCohen">Kevin Bretonnel Cohen</a><script data-card-contents-for-user="3430318" type="text/json">{"id":3430318,"first_name":"Kevin Bretonnel","last_name":"Cohen","domain_name":"ucdenver","page_name":"KevinBCohen","display_name":"Kevin Bretonnel Cohen","profile_url":"https://ucdenver.academia.edu/KevinBCohen?f_ri=1432","photo":"https://0.academia-photos.com/3430318/2822867/3294558/s65_kevin.b._cohen.jpg"}</script></span></span></li><li class="js-paper-rank-work_76515331 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="76515331"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 76515331, container: ".js-paper-rank-work_76515331", }); 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God has excelled in creating this life in its most beautiful form, making the difference of people and the difference of graces among them a cornerstone for... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_61006969" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Life is a blessing from the graces of God that he granted to all His creatures. God has excelled in creating this life in its most beautiful form, making the difference of people and the difference of graces among them a cornerstone for enjoying life. Created nature around him, which fascinates hearts, and some enjoy hearing the melody of nature and the chirping of birds and the chants of the seas. The situation regarding the deaf and dumb category is different, because life on a daily basis faces the deaf and dumb category with various difficulties, some of which are similar to what other natural individuals face, Some of the others are his own, that is, they are caused by the problems he suffers because of his disability, and the deaf-dumb tries to continue living relying on his attempt to be able to adapt to the conditions of his life, so they succeed and fail at times. The greater challenge for the deafdumb is to strive all possible ways to communicate with the society easily and confidently. They face many problems because most of the individuals they interact may lack knowledge of communicating using sign language. This research aims to reach solutions to overcome this problem by proposing an application to communicate for the Deaf and dumb. This application will help the public to understand the sign language used by deaf and dumb individuals. Therefore, the deaf and dumb group can communicate with people who do not know sign language without feeling embarrassed.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/61006969" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="338ff843fda4af8e885f2610ea94930b" rel="nofollow" data-download="{"attachment_id":74201492,"asset_id":61006969,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/74201492/download_file?st=MTczOTgzODMzOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="13733577" rel="nofollow" href="https://iiitb.academia.edu/IJIRAEInternationalJournalofInnovativeResearchinAdvancedEngineering">IJIRAE - International Journal of Innovative Research in Advanced Engineering</a><script data-card-contents-for-user="13733577" type="text/json">{"id":13733577,"first_name":"IJIRAE","last_name":"International Journal of Innovative Research in Advanced Engineering","domain_name":"iiitb","page_name":"IJIRAEInternationalJournalofInnovativeResearchinAdvancedEngineering","display_name":"IJIRAE - International Journal of Innovative Research in Advanced Engineering","profile_url":"https://iiitb.academia.edu/IJIRAEInternationalJournalofInnovativeResearchinAdvancedEngineering?f_ri=1432","photo":"https://0.academia-photos.com/13733577/3819053/35118444/s65_ijirae.international_journal_of_innovative_research_in_advanced_engineering.jpg"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-61006969">+1</span><div class="hidden js-additional-users-61006969"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://independent.academia.edu/RayanNasser7">Rayan Nasser</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-61006969'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-61006969').html(); 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container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_61006969 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="61006969"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 61006969; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=61006969]").text(description); $(".js-view-count-work_61006969").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_61006969").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="61006969"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">6</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="1432" rel="nofollow" href="https://www.academia.edu/Documents/in/Natural_Language_Processing">Natural Language Processing</a>, <script data-card-contents-for-ri="1432" type="text/json">{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="6644" rel="nofollow" href="https://www.academia.edu/Documents/in/Sign_Languages">Sign Languages</a>, <script data-card-contents-for-ri="6644" type="text/json">{"id":6644,"name":"Sign Languages","url":"https://www.academia.edu/Documents/in/Sign_Languages?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="7258" rel="nofollow" href="https://www.academia.edu/Documents/in/Assistive_Technology">Assistive Technology</a>, <script data-card-contents-for-ri="7258" type="text/json">{"id":7258,"name":"Assistive Technology","url":"https://www.academia.edu/Documents/in/Assistive_Technology?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="11984" rel="nofollow" href="https://www.academia.edu/Documents/in/Speech_Recognition">Speech Recognition</a><script data-card-contents-for-ri="11984" type="text/json">{"id":11984,"name":"Speech Recognition","url":"https://www.academia.edu/Documents/in/Speech_Recognition?f_ri=1432","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=61006969]'), work: {"id":61006969,"title":"SMART VIDEO-BASED SIGN LANGUAGE APP: IMPACTS ON COMMUNICATION FOR DEAF AND DUMB INDIVIDUALS","created_at":"2021-11-04T07:54:31.367-07:00","url":"https://www.academia.edu/61006969/SMART_VIDEO_BASED_SIGN_LANGUAGE_APP_IMPACTS_ON_COMMUNICATION_FOR_DEAF_AND_DUMB_INDIVIDUALS?f_ri=1432","dom_id":"work_61006969","summary":"Life is a blessing from the graces of God that he granted to all His creatures. God has excelled in creating this life in its most beautiful form, making the difference of people and the difference of graces among them a cornerstone for enjoying life. Created nature around him, which fascinates hearts, and some enjoy hearing the melody of nature and the chirping of birds and the chants of the seas. The situation regarding the deaf and dumb category is different, because life on a daily basis faces the deaf and dumb category with various difficulties, some of which are similar to what other natural individuals face, Some of the others are his own, that is, they are caused by the problems he suffers because of his disability, and the deaf-dumb tries to continue living relying on his attempt to be able to adapt to the conditions of his life, so they succeed and fail at times. The greater challenge for the deafdumb is to strive all possible ways to communicate with the society easily and confidently. They face many problems because most of the individuals they interact may lack knowledge of communicating using sign language. This research aims to reach solutions to overcome this problem by proposing an application to communicate for the Deaf and dumb. This application will help the public to understand the sign language used by deaf and dumb individuals. Therefore, the deaf and dumb group can communicate with people who do not know sign language without feeling embarrassed.","downloadable_attachments":[{"id":74201492,"asset_id":61006969,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":13733577,"first_name":"IJIRAE","last_name":"International Journal of Innovative Research in Advanced Engineering","domain_name":"iiitb","page_name":"IJIRAEInternationalJournalofInnovativeResearchinAdvancedEngineering","display_name":"IJIRAE - International Journal of Innovative Research in Advanced Engineering","profile_url":"https://iiitb.academia.edu/IJIRAEInternationalJournalofInnovativeResearchinAdvancedEngineering?f_ri=1432","photo":"https://0.academia-photos.com/13733577/3819053/35118444/s65_ijirae.international_journal_of_innovative_research_in_advanced_engineering.jpg"},{"id":208262405,"first_name":"Rayan","last_name":"Nasser","domain_name":"independent","page_name":"RayanNasser7","display_name":"Rayan Nasser","profile_url":"https://independent.academia.edu/RayanNasser7?f_ri=1432","photo":"https://0.academia-photos.com/208262405/68308073/56687643/s65_rayan.nasser.png"}],"research_interests":[{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true},{"id":6644,"name":"Sign Languages","url":"https://www.academia.edu/Documents/in/Sign_Languages?f_ri=1432","nofollow":true},{"id":7258,"name":"Assistive Technology","url":"https://www.academia.edu/Documents/in/Assistive_Technology?f_ri=1432","nofollow":true},{"id":11984,"name":"Speech Recognition","url":"https://www.academia.edu/Documents/in/Speech_Recognition?f_ri=1432","nofollow":true},{"id":17701,"name":"Gesture Recognition","url":"https://www.academia.edu/Documents/in/Gesture_Recognition?f_ri=1432"},{"id":220007,"name":"Arabic Natural Language Processing","url":"https://www.academia.edu/Documents/in/Arabic_Natural_Language_Processing?f_ri=1432"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_53042151" data-work_id="53042151" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/53042151/A_Semantic_Taxonomy_for_Weighting_Assumptions_to_Reduce_Feature_Selection_from_Social_Media_and_Forum_Posts">A Semantic Taxonomy for Weighting Assumptions to Reduce Feature Selection from Social Media and Forum Posts</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Numerous researchers have worked on the knowledge-based semantics of words to clarify the ambiguity of (<a href="https://github.com/alimuttaleb/" rel="nofollow">https://github.com/alimuttaleb/</a> Ali-Muttaleb/blob/master/Synonym.txt) synonyms in various natural-language processing fields, such as... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_53042151" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Numerous researchers have worked on the knowledge-based semantics of words to clarify the ambiguity of (<a href="https://github.com/alimuttaleb/" rel="nofollow">https://github.com/alimuttaleb/</a> Ali-Muttaleb/blob/master/Synonym.txt) synonyms in various natural-language processing fields, such as Wikipedia, websites, and social networks. This paper attempts to clarify ambiguities in the lexical semantics of taxonomy in social media. It proposes a new knowledge-based semantic representation approach that can handle ambiguity and high dimensionality issues in text mining. The proposed approach consists of two main components, namely, a feature-based method for incorporating the relationships between lexical sources and a topicbased reduction method to overcome high dimensionality issues. These components help weight and reduce the relevant features of a concept. The proposed approach captures further lexical semantic similarity between words. It also evaluates the use of (<a href="https://wordnet.princeton.edu" rel="nofollow">https://wordnet.princeton.edu</a>) WordNet 3.1 in text clustering and constant weighting assumption in the feature-based method used to select concepts/words from social media. To address ambiguity, the semantics of concepts with small feature subset size reduction are represented, and the performance of the semantic similarity measurement is improved. The proposed method evaluates word semantic similarity using the (<a href="https://github.com/" rel="nofollow">https://github.com/</a> alimuttaleb/semantictaxonomy/blob/master/mc30.txt) MC30 dataset in Word-Net and obtains the following results for semantic representation: r = 0.82, p = 0.81, m = 0.81, and nz = 0.96.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/53042151" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="0f1c48bb05caa2171370927880389f07" rel="nofollow" data-download="{"attachment_id":70012224,"asset_id":53042151,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/70012224/download_file?st=MTczOTgzODMzOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="203541451" href="https://ump.academia.edu/AliMHasan">Ali M. Hasan</a><script data-card-contents-for-user="203541451" type="text/json">{"id":203541451,"first_name":"Ali","last_name":"M. Hasan","domain_name":"ump","page_name":"AliMHasan","display_name":"Ali M. Hasan","profile_url":"https://ump.academia.edu/AliMHasan?f_ri=1432","photo":"https://0.academia-photos.com/203541451/64475800/52798066/s65_ali.m._hasan.jpg"}</script></span></span></li><li class="js-paper-rank-work_53042151 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="53042151"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 53042151, container: ".js-paper-rank-work_53042151", }); });</script></li><li class="js-percentile-work_53042151 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 53042151; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_53042151"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_53042151 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="53042151"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 53042151; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=53042151]").text(description); $(".js-view-count-work_53042151").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_53042151").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="53042151"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">3</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="1432" rel="nofollow" href="https://www.academia.edu/Documents/in/Natural_Language_Processing">Natural Language Processing</a>, <script data-card-contents-for-ri="1432" type="text/json">{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="2349" rel="nofollow" href="https://www.academia.edu/Documents/in/Semantics">Semantics</a>, <script data-card-contents-for-ri="2349" type="text/json">{"id":2349,"name":"Semantics","url":"https://www.academia.edu/Documents/in/Semantics?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="456942" rel="nofollow" href="https://www.academia.edu/Documents/in/Semantic_representation">Semantic representation</a><script data-card-contents-for-ri="456942" type="text/json">{"id":456942,"name":"Semantic representation","url":"https://www.academia.edu/Documents/in/Semantic_representation?f_ri=1432","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=53042151]'), work: {"id":53042151,"title":"A Semantic Taxonomy for Weighting Assumptions to Reduce Feature Selection from Social Media and Forum Posts","created_at":"2021-09-20T14:40:16.863-07:00","url":"https://www.academia.edu/53042151/A_Semantic_Taxonomy_for_Weighting_Assumptions_to_Reduce_Feature_Selection_from_Social_Media_and_Forum_Posts?f_ri=1432","dom_id":"work_53042151","summary":"Numerous researchers have worked on the knowledge-based semantics of words to clarify the ambiguity of (https://github.com/alimuttaleb/ Ali-Muttaleb/blob/master/Synonym.txt) synonyms in various natural-language processing fields, such as Wikipedia, websites, and social networks. This paper attempts to clarify ambiguities in the lexical semantics of taxonomy in social media. It proposes a new knowledge-based semantic representation approach that can handle ambiguity and high dimensionality issues in text mining. The proposed approach consists of two main components, namely, a feature-based method for incorporating the relationships between lexical sources and a topicbased reduction method to overcome high dimensionality issues. These components help weight and reduce the relevant features of a concept. The proposed approach captures further lexical semantic similarity between words. It also evaluates the use of (https://wordnet.princeton.edu) WordNet 3.1 in text clustering and constant weighting assumption in the feature-based method used to select concepts/words from social media. To address ambiguity, the semantics of concepts with small feature subset size reduction are represented, and the performance of the semantic similarity measurement is improved. The proposed method evaluates word semantic similarity using the (https://github.com/ alimuttaleb/semantictaxonomy/blob/master/mc30.txt) MC30 dataset in Word-Net and obtains the following results for semantic representation: r = 0.82, p = 0.81, m = 0.81, and nz = 0.96.","downloadable_attachments":[{"id":70012224,"asset_id":53042151,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":203541451,"first_name":"Ali","last_name":"M. Hasan","domain_name":"ump","page_name":"AliMHasan","display_name":"Ali M. Hasan","profile_url":"https://ump.academia.edu/AliMHasan?f_ri=1432","photo":"https://0.academia-photos.com/203541451/64475800/52798066/s65_ali.m._hasan.jpg"}],"research_interests":[{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true},{"id":2349,"name":"Semantics","url":"https://www.academia.edu/Documents/in/Semantics?f_ri=1432","nofollow":true},{"id":456942,"name":"Semantic representation","url":"https://www.academia.edu/Documents/in/Semantic_representation?f_ri=1432","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_49397606" data-work_id="49397606" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/49397606/T%C3%BCmevarimli_Mantik_Programlama_%C4%B0le_T%C3%BCrk%C3%A7e_%C4%B0%C3%A7in_Kelime_Anlami_Belirginle%C5%9Ftirme_Uygulamasi">Tümevarimli Mantik Programlama İle Türkçe İçin Kelime Anlami Belirginleştirme Uygulamasi</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Word sense disambiguation (WSD) is the problem of determining the sense of a multi-sense word in a given context. It is one of the important processes needed for natural language processing applications. Inductive Logic Programming (ILP)... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_49397606" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Word sense disambiguation (WSD) is the problem of determining the sense of a multi-sense word in a given context. It is one of the important processes needed for natural language processing applications. Inductive Logic Programming (ILP) is the area of artificial intelligence which contains machine learning and logic programming. It aims to build first- order theories from examples and background knowledge, which are also represented by first- order clauses. The richness of first order logic employed in ILP can hopefully provide advantages for NLP applications such as WSD. In this study, a corpus-based WSD application is improved by using ILP.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/49397606" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="7cc2f9d78e7f08f95a096f084e249155" rel="nofollow" data-download="{"attachment_id":67761874,"asset_id":49397606,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/67761874/download_file?st=MTczOTgzODMzOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="37432050" href="https://trakya.academia.edu/YilmazKilicaslan">Yilmaz Kilicaslan</a><script data-card-contents-for-user="37432050" type="text/json">{"id":37432050,"first_name":"Yilmaz","last_name":"Kilicaslan","domain_name":"trakya","page_name":"YilmazKilicaslan","display_name":"Yilmaz Kilicaslan","profile_url":"https://trakya.academia.edu/YilmazKilicaslan?f_ri=1432","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_49397606 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="49397606"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 49397606, container: ".js-paper-rank-work_49397606", }); 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$(".js-view-count[data-work-id=49397606]").text(description); $(".js-view-count-work_49397606").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_49397606").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="49397606"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">8</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="1432" rel="nofollow" href="https://www.academia.edu/Documents/in/Natural_Language_Processing">Natural Language Processing</a>, <script data-card-contents-for-ri="1432" type="text/json">{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="2008" rel="nofollow" href="https://www.academia.edu/Documents/in/Machine_Learning">Machine Learning</a>, <script data-card-contents-for-ri="2008" type="text/json">{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="9840" rel="nofollow" href="https://www.academia.edu/Documents/in/Word_Sense_Disambiguation">Word Sense Disambiguation</a>, <script data-card-contents-for-ri="9840" type="text/json">{"id":9840,"name":"Word Sense Disambiguation","url":"https://www.academia.edu/Documents/in/Word_Sense_Disambiguation?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="20858" rel="nofollow" href="https://www.academia.edu/Documents/in/Inductive_Logic_Programming">Inductive Logic Programming</a><script data-card-contents-for-ri="20858" type="text/json">{"id":20858,"name":"Inductive Logic Programming","url":"https://www.academia.edu/Documents/in/Inductive_Logic_Programming?f_ri=1432","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=49397606]'), work: {"id":49397606,"title":"Tümevarimli Mantik Programlama İle Türkçe İçin Kelime Anlami Belirginleştirme Uygulamasi","created_at":"2021-06-26T08:01:40.087-07:00","url":"https://www.academia.edu/49397606/T%C3%BCmevarimli_Mantik_Programlama_%C4%B0le_T%C3%BCrk%C3%A7e_%C4%B0%C3%A7in_Kelime_Anlami_Belirginle%C5%9Ftirme_Uygulamasi?f_ri=1432","dom_id":"work_49397606","summary":"Word sense disambiguation (WSD) is the problem of determining the sense of a multi-sense word in a given context. It is one of the important processes needed for natural language processing applications. Inductive Logic Programming (ILP) is the area of artificial intelligence which contains machine learning and logic programming. It aims to build first- order theories from examples and background knowledge, which are also represented by first- order clauses. The richness of first order logic employed in ILP can hopefully provide advantages for NLP applications such as WSD. In this study, a corpus-based WSD application is improved by using ILP.","downloadable_attachments":[{"id":67761874,"asset_id":49397606,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":37432050,"first_name":"Yilmaz","last_name":"Kilicaslan","domain_name":"trakya","page_name":"YilmazKilicaslan","display_name":"Yilmaz Kilicaslan","profile_url":"https://trakya.academia.edu/YilmazKilicaslan?f_ri=1432","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true},{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=1432","nofollow":true},{"id":9840,"name":"Word Sense Disambiguation","url":"https://www.academia.edu/Documents/in/Word_Sense_Disambiguation?f_ri=1432","nofollow":true},{"id":20858,"name":"Inductive Logic Programming","url":"https://www.academia.edu/Documents/in/Inductive_Logic_Programming?f_ri=1432","nofollow":true},{"id":101530,"name":"Artificial Intelligent","url":"https://www.academia.edu/Documents/in/Artificial_Intelligent?f_ri=1432"},{"id":181847,"name":"First-Order Logic","url":"https://www.academia.edu/Documents/in/First-Order_Logic?f_ri=1432"},{"id":405178,"name":"First Order Logic","url":"https://www.academia.edu/Documents/in/First_Order_Logic?f_ri=1432"},{"id":1036563,"name":"Background Knowledge","url":"https://www.academia.edu/Documents/in/Background_Knowledge?f_ri=1432"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_47695923" data-work_id="47695923" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/47695923/Automatic_stochastic_tagging_of_natural_language_texts">Automatic stochastic tagging of natural language texts</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Five language and tagset independent stochastic taggers, handling morphological and contextual information, are presented and tested in corpora of seven European languages (Dutch, English, French, German, Greek, Italian and Spanish),... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_47695923" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Five language and tagset independent stochastic taggers, handling morphological and contextual information, are presented and tested in corpora of seven European languages (Dutch, English, French, German, Greek, Italian and Spanish), using two sets of grammatical tags; a small set containing the eleven main grammatical classes and a large set of grammatical categories common to all languages. The unknown words are tagged using an experimentally proven stochastic hypothesis that links the stochastic behavior of the unknown words with that of the less probable known words. A fully automatic training and tagging program has been implemented on an IBM PC-compatible 80386-based computer. Measurements of error rate, time response, and memory requirements have shown that the taggers" performance is satisfactory, even though a small training text is available. The error rate is improved when new texts are used to update the stochastic model parameters.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/47695923" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="2adf75ef96d967ee590149010871b973" rel="nofollow" data-download="{"attachment_id":66655650,"asset_id":47695923,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/66655650/download_file?st=MTczOTgzODMzOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="44508070" href="https://independent.academia.edu/Kokkinakis">George Kokkinakis</a><script data-card-contents-for-user="44508070" type="text/json">{"id":44508070,"first_name":"George","last_name":"Kokkinakis","domain_name":"independent","page_name":"Kokkinakis","display_name":"George Kokkinakis","profile_url":"https://independent.academia.edu/Kokkinakis?f_ri=1432","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_47695923 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="47695923"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 47695923, container: ".js-paper-rank-work_47695923", }); 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a small set containing the eleven main grammatical classes and a large set of grammatical categories common to all languages. The unknown words are tagged using an experimentally proven stochastic hypothesis that links the stochastic behavior of the unknown words with that of the less probable known words. A fully automatic training and tagging program has been implemented on an IBM PC-compatible 80386-based computer. Measurements of error rate, time response, and memory requirements have shown that the taggers\" performance is satisfactory, even though a small training text is available. The error rate is improved when new texts are used to update the stochastic model parameters.","downloadable_attachments":[{"id":66655650,"asset_id":47695923,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":44508070,"first_name":"George","last_name":"Kokkinakis","domain_name":"independent","page_name":"Kokkinakis","display_name":"George Kokkinakis","profile_url":"https://independent.academia.edu/Kokkinakis?f_ri=1432","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true},{"id":3268,"name":"Computational Linguistics","url":"https://www.academia.edu/Documents/in/Computational_Linguistics?f_ri=1432","nofollow":true},{"id":97618,"name":"Natural language","url":"https://www.academia.edu/Documents/in/Natural_language?f_ri=1432","nofollow":true},{"id":164637,"name":"Bit Error Rate","url":"https://www.academia.edu/Documents/in/Bit_Error_Rate?f_ri=1432","nofollow":true},{"id":871199,"name":"Stochastic Model","url":"https://www.academia.edu/Documents/in/Stochastic_Model?f_ri=1432"},{"id":2467595,"name":"Probabilistic Model","url":"https://www.academia.edu/Documents/in/Probabilistic_Model?f_ri=1432"},{"id":2476504,"name":"Contextual Information","url":"https://www.academia.edu/Documents/in/Contextual_Information?f_ri=1432"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_43726378" data-work_id="43726378" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/43726378/Domain_Driven_Word_Sense_Disambiguation">Domain Driven Word Sense Disambiguation</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Many times people use a single word with multiple senses, which provides a different meaning based on the sentence in which it has been used. So, the main goal of the work is to disambiguate an ambiguous word that has been located in... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_43726378" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Many times people use a single word with multiple senses, which provides a different meaning based on the sentence in which it has been used. So, the main goal of the work is to disambiguate an ambiguous word that has been located in certain sentences. Thus, it helps in exploring the actual sense of the word that it means. This process is itself named as Word Sense Disambiguation (WSD) which is an essential and ongoing subject in NLP. However, the role of domain is very helpful in exploring the actual sense of an ambiguous word. There are a number of approaches to WSD which takes a wider semantic space of ambiguous words into account [10]. This semantic space can be represented as a specific domain, task or application [10]. The domain information which is one among them is more advantageous in the process of disambiguation, thus, the work explores the role of the domain in the disambiguation process. The work also includes a score allotment to all the senses of an ambiguous word based on the semantic relation of it with the ambiguous word. Later, this helps in obtaining the actual sense of the word. Keywords: Disambiguation, natural language processing, word sense I. INTRODUCTION In NLP, ambiguity has become a barrier to human language understanding. The best solution to overcome this barrier is the Word Sense Disambiguation process. For instance, consider an example word 'bank' which has two senses. One sense of the word is "financial reservoir" and another sense of it is "a river edge". Now consider the following two statements. "Willows lined the bank of the stream" and "I went to the bank to get a home loan" To a human, the difference between these two senses of the word 'bank' will be clearly understandable as it means 'river bank' in the first sentence and a 'financial reservoir' in the second sentence. But this is not the same in case of a machine. Thus, it needs a different solution. There are different solutions of WSD that have been proposed. This can be generally divided into supervised and knowledge-based approaches. In comparison of these two approaches, the knowledge-based approach has gained a rapid development while compared to others in recent years [12]. Also, the availability of abundant information from a different knowledge resources has narrowed the gap between these two approaches [12]. Hence, the use of a knowledge-based approach has been considered as a useful one in our work. The main idea of this approach is to make use of WordNet and a semantic space such as specific domains to a greater extent to obtain the actual sense of the ambiguous word. Thus, the main purpose of our work is to make use of the domain information as a best possible way to explore the actual meaning of an ambiguous word that has been used in a sentence. There is a hypothesis that the domain information provides a powerful way to establish a semantic relation among the word senses [11]. Thus, it can be used in a profitable way in the process of disambiguation. We can refer to the domain, as a set of words that has a strong semantic relation between them. Thus, an approach of using domain information in obtaining the actual sense of an ambiguous word makes sense. Here, the basic prediction that we assumed to achieve the goal was to make use of a distinct score allotment for each of the senses that comes under the semantic space of domain of ambiguous word. This score allotment is not a random one, rather the sense that is closer to the word in a particular sentence will be allotted with a highest score while the remaining senses will be allotted in a decreasing manner accordingly. This prediction gave a way to attain the result later. Also, this approach helps in less time consumption than the normal disambiguation process. Therefore, the efficiency of the project will also be increased. II. LITERATURE SURVEY S.G Kolte and S.G Bhirud approaches is to Word sense disambiguation using wordnet domains, here they used wordnet as database to define domains and the words in the given sentence is taken as parameter which helps to detect domain by domain-oriented text analysis. For this they go through unsupervised method, and they trying to disambiguate nouns first using pos tag and getting results but here the drawback is that this approach is failed for a word having more than one sense in a same domain [1]. A Fully Unsupervised Word sense disambiguation system using dependency knowledge on specific domain is proposed in the year 2010, here they developed a fully unsupervised system using domain specific knowledge and this system performs above the first sense baseline. they showed that wsd can be achieve in unsupervised method without get compromised with supervised approach by using easily available unannotated text from internet and other sources and get a good result [2].</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/43726378" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="0583460d2800d4ecd5f7af3a443f5ad7" rel="nofollow" data-download="{"attachment_id":64035181,"asset_id":43726378,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/64035181/download_file?st=MTczOTgzODMzOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="6079060" href="https://independent.academia.edu/IJRASETPublication">IJRASET Publication</a><script data-card-contents-for-user="6079060" type="text/json">{"id":6079060,"first_name":"IJRASET","last_name":"Publication","domain_name":"independent","page_name":"IJRASETPublication","display_name":"IJRASET Publication","profile_url":"https://independent.academia.edu/IJRASETPublication?f_ri=1432","photo":"https://0.academia-photos.com/6079060/2549300/33111525/s65_ijraset.publication.jpg"}</script></span></span></li><li class="js-paper-rank-work_43726378 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="43726378"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 43726378, container: ".js-paper-rank-work_43726378", }); 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$(".js-view-count[data-work-id=43726378]").text(description); $(".js-view-count-work_43726378").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_43726378").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="43726378"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">4</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="1432" rel="nofollow" href="https://www.academia.edu/Documents/in/Natural_Language_Processing">Natural Language Processing</a>, <script data-card-contents-for-ri="1432" type="text/json">{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="9840" rel="nofollow" href="https://www.academia.edu/Documents/in/Word_Sense_Disambiguation">Word Sense Disambiguation</a>, <script data-card-contents-for-ri="9840" type="text/json">{"id":9840,"name":"Word Sense Disambiguation","url":"https://www.academia.edu/Documents/in/Word_Sense_Disambiguation?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="118883" rel="nofollow" href="https://www.academia.edu/Documents/in/Disambiguation">Disambiguation</a>, <script data-card-contents-for-ri="118883" type="text/json">{"id":118883,"name":"Disambiguation","url":"https://www.academia.edu/Documents/in/Disambiguation?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="220007" rel="nofollow" href="https://www.academia.edu/Documents/in/Arabic_Natural_Language_Processing">Arabic Natural Language Processing</a><script data-card-contents-for-ri="220007" type="text/json">{"id":220007,"name":"Arabic Natural Language Processing","url":"https://www.academia.edu/Documents/in/Arabic_Natural_Language_Processing?f_ri=1432","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=43726378]'), work: {"id":43726378,"title":"Domain Driven Word Sense Disambiguation","created_at":"2020-07-27T21:46:29.592-07:00","url":"https://www.academia.edu/43726378/Domain_Driven_Word_Sense_Disambiguation?f_ri=1432","dom_id":"work_43726378","summary":"Many times people use a single word with multiple senses, which provides a different meaning based on the sentence in which it has been used. So, the main goal of the work is to disambiguate an ambiguous word that has been located in certain sentences. Thus, it helps in exploring the actual sense of the word that it means. This process is itself named as Word Sense Disambiguation (WSD) which is an essential and ongoing subject in NLP. However, the role of domain is very helpful in exploring the actual sense of an ambiguous word. There are a number of approaches to WSD which takes a wider semantic space of ambiguous words into account [10]. This semantic space can be represented as a specific domain, task or application [10]. The domain information which is one among them is more advantageous in the process of disambiguation, thus, the work explores the role of the domain in the disambiguation process. The work also includes a score allotment to all the senses of an ambiguous word based on the semantic relation of it with the ambiguous word. Later, this helps in obtaining the actual sense of the word. Keywords: Disambiguation, natural language processing, word sense I. INTRODUCTION In NLP, ambiguity has become a barrier to human language understanding. The best solution to overcome this barrier is the Word Sense Disambiguation process. For instance, consider an example word 'bank' which has two senses. One sense of the word is \"financial reservoir\" and another sense of it is \"a river edge\". Now consider the following two statements. \"Willows lined the bank of the stream\" and \"I went to the bank to get a home loan\" To a human, the difference between these two senses of the word 'bank' will be clearly understandable as it means 'river bank' in the first sentence and a 'financial reservoir' in the second sentence. But this is not the same in case of a machine. Thus, it needs a different solution. There are different solutions of WSD that have been proposed. This can be generally divided into supervised and knowledge-based approaches. In comparison of these two approaches, the knowledge-based approach has gained a rapid development while compared to others in recent years [12]. Also, the availability of abundant information from a different knowledge resources has narrowed the gap between these two approaches [12]. Hence, the use of a knowledge-based approach has been considered as a useful one in our work. The main idea of this approach is to make use of WordNet and a semantic space such as specific domains to a greater extent to obtain the actual sense of the ambiguous word. Thus, the main purpose of our work is to make use of the domain information as a best possible way to explore the actual meaning of an ambiguous word that has been used in a sentence. There is a hypothesis that the domain information provides a powerful way to establish a semantic relation among the word senses [11]. Thus, it can be used in a profitable way in the process of disambiguation. We can refer to the domain, as a set of words that has a strong semantic relation between them. Thus, an approach of using domain information in obtaining the actual sense of an ambiguous word makes sense. Here, the basic prediction that we assumed to achieve the goal was to make use of a distinct score allotment for each of the senses that comes under the semantic space of domain of ambiguous word. This score allotment is not a random one, rather the sense that is closer to the word in a particular sentence will be allotted with a highest score while the remaining senses will be allotted in a decreasing manner accordingly. This prediction gave a way to attain the result later. Also, this approach helps in less time consumption than the normal disambiguation process. Therefore, the efficiency of the project will also be increased. II. LITERATURE SURVEY S.G Kolte and S.G Bhirud approaches is to Word sense disambiguation using wordnet domains, here they used wordnet as database to define domains and the words in the given sentence is taken as parameter which helps to detect domain by domain-oriented text analysis. For this they go through unsupervised method, and they trying to disambiguate nouns first using pos tag and getting results but here the drawback is that this approach is failed for a word having more than one sense in a same domain [1]. A Fully Unsupervised Word sense disambiguation system using dependency knowledge on specific domain is proposed in the year 2010, here they developed a fully unsupervised system using domain specific knowledge and this system performs above the first sense baseline. they showed that wsd can be achieve in unsupervised method without get compromised with supervised approach by using easily available unannotated text from internet and other sources and get a good result [2].","downloadable_attachments":[{"id":64035181,"asset_id":43726378,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":6079060,"first_name":"IJRASET","last_name":"Publication","domain_name":"independent","page_name":"IJRASETPublication","display_name":"IJRASET Publication","profile_url":"https://independent.academia.edu/IJRASETPublication?f_ri=1432","photo":"https://0.academia-photos.com/6079060/2549300/33111525/s65_ijraset.publication.jpg"}],"research_interests":[{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true},{"id":9840,"name":"Word Sense Disambiguation","url":"https://www.academia.edu/Documents/in/Word_Sense_Disambiguation?f_ri=1432","nofollow":true},{"id":118883,"name":"Disambiguation","url":"https://www.academia.edu/Documents/in/Disambiguation?f_ri=1432","nofollow":true},{"id":220007,"name":"Arabic Natural Language Processing","url":"https://www.academia.edu/Documents/in/Arabic_Natural_Language_Processing?f_ri=1432","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_43048678" data-work_id="43048678" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/43048678/Combining_Machine_Learning_Classifiers_for_the_Task_of_Arabic_Characters_Recognition">Combining Machine Learning Classifiers for the Task of Arabic Characters Recognition</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">There is a number of machine learning algorithms for recognizing Arabic characters. In this paper, we investigate a range of strategies for multiple machine learning algorithms for the task of Arabic characters recognition, where we are... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_43048678" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">There is a number of machine learning algorithms for recognizing Arabic characters. In this paper, we investigate a range of strategies for multiple machine learning algorithms for the task of Arabic characters recognition, where we are faced with imperfect and dimensionally variable input characters. We show two different strategies to combining multiple machine learning algorithms: manual backoff strategry and ensemble learning strategy. We show the performance of using individual algorithms and combined algorithms on recognizing Arabic characters. Experimental results show that combined confidence-based strategies can produce more accurate results than each algorithm produces by itself and even the ones exhibited by the majority voting combination.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/43048678" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="87464344271e1d8db46cb0b37ac71f71" rel="nofollow" data-download="{"attachment_id":63307953,"asset_id":43048678,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/63307953/download_file?st=MTczOTgzODMzOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="4414965" href="https://manchester.academia.edu/SardarJaf">Sardar Jaf</a><script data-card-contents-for-user="4414965" type="text/json">{"id":4414965,"first_name":"Sardar","last_name":"Jaf","domain_name":"manchester","page_name":"SardarJaf","display_name":"Sardar Jaf","profile_url":"https://manchester.academia.edu/SardarJaf?f_ri=1432","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_43048678 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="43048678"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 43048678, container: ".js-paper-rank-work_43048678", }); 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$(".js-view-count[data-work-id=43048678]").text(description); $(".js-view-count-work_43048678").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_43048678").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="43048678"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">9</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="1432" rel="nofollow" href="https://www.academia.edu/Documents/in/Natural_Language_Processing">Natural Language Processing</a>, <script data-card-contents-for-ri="1432" type="text/json">{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="2008" rel="nofollow" href="https://www.academia.edu/Documents/in/Machine_Learning">Machine Learning</a>, <script data-card-contents-for-ri="2008" type="text/json">{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="2185" rel="nofollow" href="https://www.academia.edu/Documents/in/Natural_Language_Generation">Natural Language Generation</a>, <script data-card-contents-for-ri="2185" type="text/json">{"id":2185,"name":"Natural Language Generation","url":"https://www.academia.edu/Documents/in/Natural_Language_Generation?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="5111" rel="nofollow" href="https://www.academia.edu/Documents/in/Character_Recognition">Character Recognition</a><script data-card-contents-for-ri="5111" type="text/json">{"id":5111,"name":"Character Recognition","url":"https://www.academia.edu/Documents/in/Character_Recognition?f_ri=1432","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=43048678]'), work: {"id":43048678,"title":"Combining Machine Learning Classifiers for the Task of Arabic Characters Recognition","created_at":"2020-05-14T06:08:12.772-07:00","url":"https://www.academia.edu/43048678/Combining_Machine_Learning_Classifiers_for_the_Task_of_Arabic_Characters_Recognition?f_ri=1432","dom_id":"work_43048678","summary":"There is a number of machine learning algorithms for recognizing Arabic characters. In this paper, we investigate a range of strategies for multiple machine learning algorithms for the task of Arabic characters recognition, where we are faced with imperfect and dimensionally variable input characters. We show two different strategies to combining multiple machine learning algorithms: manual backoff strategry and ensemble learning strategy. We show the performance of using individual algorithms and combined algorithms on recognizing Arabic characters. Experimental results show that combined confidence-based strategies can produce more accurate results than each algorithm produces by itself and even the ones exhibited by the majority voting combination.","downloadable_attachments":[{"id":63307953,"asset_id":43048678,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":4414965,"first_name":"Sardar","last_name":"Jaf","domain_name":"manchester","page_name":"SardarJaf","display_name":"Sardar Jaf","profile_url":"https://manchester.academia.edu/SardarJaf?f_ri=1432","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true},{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=1432","nofollow":true},{"id":2185,"name":"Natural Language Generation","url":"https://www.academia.edu/Documents/in/Natural_Language_Generation?f_ri=1432","nofollow":true},{"id":5111,"name":"Character Recognition","url":"https://www.academia.edu/Documents/in/Character_Recognition?f_ri=1432","nofollow":true},{"id":220007,"name":"Arabic Natural Language Processing","url":"https://www.academia.edu/Documents/in/Arabic_Natural_Language_Processing?f_ri=1432"},{"id":265584,"name":"Optical Character Recognition","url":"https://www.academia.edu/Documents/in/Optical_Character_Recognition?f_ri=1432"},{"id":515065,"name":"Arabic Characters Recognition","url":"https://www.academia.edu/Documents/in/Arabic_Characters_Recognition?f_ri=1432"},{"id":915000,"name":"Intelligent Character Recognition","url":"https://www.academia.edu/Documents/in/Intelligent_Character_Recognition?f_ri=1432"},{"id":1946993,"name":"Offline Handwriting Arabic Character","url":"https://www.academia.edu/Documents/in/Offline_Handwriting_Arabic_Character?f_ri=1432"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_518774" data-work_id="518774" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/518774/Upper_modeling_A_general_organization_of_knowledge_for_natural_language_processing">Upper modeling: A general organization of knowledge for natural language processing</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/518774" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="eb47481c6e69eda91340b781cf77c0e6" rel="nofollow" data-download="{"attachment_id":2497124,"asset_id":518774,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/2497124/download_file?st=MTczOTgzODMzOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="394201" href="https://uni-bremen.academia.edu/JohnBateman">John Bateman</a><script data-card-contents-for-user="394201" type="text/json">{"id":394201,"first_name":"John","last_name":"Bateman","domain_name":"uni-bremen","page_name":"JohnBateman","display_name":"John Bateman","profile_url":"https://uni-bremen.academia.edu/JohnBateman?f_ri=1432","photo":"https://0.academia-photos.com/394201/123449/143272/s65_john.bateman.jpg"}</script></span></span></li><li class="js-paper-rank-work_518774 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="518774"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 518774, container: ".js-paper-rank-work_518774", }); });</script></li><li class="js-percentile-work_518774 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 518774; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_518774"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_518774 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="518774"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 518774; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=518774]").text(description); $(".js-view-count-work_518774").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_518774").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="518774"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">4</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="1432" rel="nofollow" href="https://www.academia.edu/Documents/in/Natural_Language_Processing">Natural Language Processing</a>, <script data-card-contents-for-ri="1432" type="text/json">{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="33890" rel="nofollow" href="https://www.academia.edu/Documents/in/Language_Processing">Language Processing</a>, <script data-card-contents-for-ri="33890" type="text/json">{"id":33890,"name":"Language Processing","url":"https://www.academia.edu/Documents/in/Language_Processing?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="197861" rel="nofollow" href="https://www.academia.edu/Documents/in/Domain_Knowledge">Domain Knowledge</a>, <script data-card-contents-for-ri="197861" type="text/json">{"id":197861,"name":"Domain Knowledge","url":"https://www.academia.edu/Documents/in/Domain_Knowledge?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="1238618" rel="nofollow" href="https://www.academia.edu/Documents/in/Text_Generation">Text Generation</a><script data-card-contents-for-ri="1238618" type="text/json">{"id":1238618,"name":"Text Generation","url":"https://www.academia.edu/Documents/in/Text_Generation?f_ri=1432","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=518774]'), work: {"id":518774,"title":"Upper modeling: A general organization of knowledge for natural language processing","created_at":"2011-04-08T19:48:35.249-07:00","url":"https://www.academia.edu/518774/Upper_modeling_A_general_organization_of_knowledge_for_natural_language_processing?f_ri=1432","dom_id":"work_518774","summary":null,"downloadable_attachments":[{"id":2497124,"asset_id":518774,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":394201,"first_name":"John","last_name":"Bateman","domain_name":"uni-bremen","page_name":"JohnBateman","display_name":"John Bateman","profile_url":"https://uni-bremen.academia.edu/JohnBateman?f_ri=1432","photo":"https://0.academia-photos.com/394201/123449/143272/s65_john.bateman.jpg"}],"research_interests":[{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true},{"id":33890,"name":"Language Processing","url":"https://www.academia.edu/Documents/in/Language_Processing?f_ri=1432","nofollow":true},{"id":197861,"name":"Domain Knowledge","url":"https://www.academia.edu/Documents/in/Domain_Knowledge?f_ri=1432","nofollow":true},{"id":1238618,"name":"Text Generation","url":"https://www.academia.edu/Documents/in/Text_Generation?f_ri=1432","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_2246158" data-work_id="2246158" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/2246158/Analysis_of_the_IJCNN_2007_agnostic_learning_vs_prior_knowledge_challenge">Analysis of the IJCNN 2007 agnostic learning vs. prior knowledge challenge</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">We organized a challenge for IJCNN 2007 to assess the added value of prior domain knowledge in machine learning. Most commercial data mining programs accept data pre-formatted in the form of a table, with each example being encoded as a... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_2246158" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">We organized a challenge for IJCNN 2007 to assess the added value of prior domain knowledge in machine learning. Most commercial data mining programs accept data pre-formatted in the form of a table, with each example being encoded as a linear feature vector. Is it worth spending time incorporating domain knowledge in feature construction or algorithm design or can off-the-shelf programs working directly on simple low-level features do better than skilled data analysts? To answer these questions, we formatted five datasets using two data representations. The participants to the "prior knowledge" track used the raw data, with full knowledge of the meaning of the data representation. Conversely, the participants to the "agnostic learning" track used a pre-formatted data table, with no knowledge of the identity of the features. The results indicate that black-box methods using relatively unsophisticated features work quite well and rapidly approach the best attainable performances. The winners on the prior knowledge track used feature extraction strategies yielding a large number of low-level features. Incorporating prior knowledge in the form of generic coding/smoothing methods to exploit regularities in data is beneficial, but incorporating actual domain knowledge in feature construction is very time consuming and seldom leads to significant improvements. The AL vs. PK challenge web site remains open for post-challenge submissions: <a href="http://www.agnostic.inf.ethz.ch/" rel="nofollow">http://www.agnostic.inf.ethz.ch/</a> .</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/2246158" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="697b3ff600ecdc1c8449a8b64a0dff40" rel="nofollow" data-download="{"attachment_id":30297821,"asset_id":2246158,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/30297821/download_file?st=MTczOTgzODMzOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="2850232" href="https://eastanglia.academia.edu/GavinCawley">Gavin Cawley</a><script data-card-contents-for-user="2850232" type="text/json">{"id":2850232,"first_name":"Gavin","last_name":"Cawley","domain_name":"eastanglia","page_name":"GavinCawley","display_name":"Gavin Cawley","profile_url":"https://eastanglia.academia.edu/GavinCawley?f_ri=1432","photo":"https://gravatar.com/avatar/8f61ceff45a5656596b720ba1ce783a9?s=65"}</script></span></span></li><li class="js-paper-rank-work_2246158 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="2246158"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 2246158, container: ".js-paper-rank-work_2246158", }); 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$(".js-view-count[data-work-id=2246158]").text(description); $(".js-view-count-work_2246158").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_2246158").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="2246158"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">26</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="465" rel="nofollow" href="https://www.academia.edu/Documents/in/Artificial_Intelligence">Artificial Intelligence</a>, <script data-card-contents-for-ri="465" type="text/json">{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="1432" rel="nofollow" href="https://www.academia.edu/Documents/in/Natural_Language_Processing">Natural Language Processing</a>, <script data-card-contents-for-ri="1432" type="text/json">{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="2008" rel="nofollow" href="https://www.academia.edu/Documents/in/Machine_Learning">Machine Learning</a>, <script data-card-contents-for-ri="2008" type="text/json">{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="2009" rel="nofollow" href="https://www.academia.edu/Documents/in/Data_Mining">Data Mining</a><script data-card-contents-for-ri="2009" type="text/json">{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=1432","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=2246158]'), work: {"id":2246158,"title":"Analysis of the IJCNN 2007 agnostic learning vs. prior knowledge challenge","created_at":"2012-12-05T22:44:31.401-08:00","url":"https://www.academia.edu/2246158/Analysis_of_the_IJCNN_2007_agnostic_learning_vs_prior_knowledge_challenge?f_ri=1432","dom_id":"work_2246158","summary":"We organized a challenge for IJCNN 2007 to assess the added value of prior domain knowledge in machine learning. Most commercial data mining programs accept data pre-formatted in the form of a table, with each example being encoded as a linear feature vector. Is it worth spending time incorporating domain knowledge in feature construction or algorithm design or can off-the-shelf programs working directly on simple low-level features do better than skilled data analysts? To answer these questions, we formatted five datasets using two data representations. The participants to the \"prior knowledge\" track used the raw data, with full knowledge of the meaning of the data representation. Conversely, the participants to the \"agnostic learning\" track used a pre-formatted data table, with no knowledge of the identity of the features. The results indicate that black-box methods using relatively unsophisticated features work quite well and rapidly approach the best attainable performances. The winners on the prior knowledge track used feature extraction strategies yielding a large number of low-level features. Incorporating prior knowledge in the form of generic coding/smoothing methods to exploit regularities in data is beneficial, but incorporating actual domain knowledge in feature construction is very time consuming and seldom leads to significant improvements. The AL vs. PK challenge web site remains open for post-challenge submissions: http://www.agnostic.inf.ethz.ch/ .","downloadable_attachments":[{"id":30297821,"asset_id":2246158,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":2850232,"first_name":"Gavin","last_name":"Cawley","domain_name":"eastanglia","page_name":"GavinCawley","display_name":"Gavin Cawley","profile_url":"https://eastanglia.academia.edu/GavinCawley?f_ri=1432","photo":"https://gravatar.com/avatar/8f61ceff45a5656596b720ba1ce783a9?s=65"}],"research_interests":[{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence?f_ri=1432","nofollow":true},{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true},{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=1432","nofollow":true},{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=1432","nofollow":true},{"id":4233,"name":"Computational Biology","url":"https://www.academia.edu/Documents/in/Computational_Biology?f_ri=1432"},{"id":10408,"name":"Support Vector Machines","url":"https://www.academia.edu/Documents/in/Support_Vector_Machines?f_ri=1432"},{"id":11598,"name":"Neural Networks","url":"https://www.academia.edu/Documents/in/Neural_Networks?f_ri=1432"},{"id":16475,"name":"Competition","url":"https://www.academia.edu/Documents/in/Competition?f_ri=1432"},{"id":28235,"name":"Multidisciplinary","url":"https://www.academia.edu/Documents/in/Multidisciplinary?f_ri=1432"},{"id":32536,"name":"Ensemble Methods","url":"https://www.academia.edu/Documents/in/Ensemble_Methods?f_ri=1432"},{"id":43774,"name":"Learning","url":"https://www.academia.edu/Documents/in/Learning?f_ri=1432"},{"id":44096,"name":"Knowledge","url":"https://www.academia.edu/Documents/in/Knowledge?f_ri=1432"},{"id":95068,"name":"Kernel Methods","url":"https://www.academia.edu/Documents/in/Kernel_Methods?f_ri=1432"},{"id":95069,"name":"Supervised Learning","url":"https://www.academia.edu/Documents/in/Supervised_Learning?f_ri=1432"},{"id":106145,"name":"Classification","url":"https://www.academia.edu/Documents/in/Classification?f_ri=1432"},{"id":116787,"name":"Algorithm Design","url":"https://www.academia.edu/Documents/in/Algorithm_Design?f_ri=1432"},{"id":138266,"name":"Boosting","url":"https://www.academia.edu/Documents/in/Boosting?f_ri=1432"},{"id":160328,"name":"Prior Knowledge","url":"https://www.academia.edu/Documents/in/Prior_Knowledge?f_ri=1432"},{"id":172418,"name":"Feature Construction","url":"https://www.academia.edu/Documents/in/Feature_Construction?f_ri=1432"},{"id":191289,"name":"Support vector machine","url":"https://www.academia.edu/Documents/in/Support_vector_machine?f_ri=1432"},{"id":194916,"name":"ROC Curve","url":"https://www.academia.edu/Documents/in/ROC_Curve?f_ri=1432"},{"id":197861,"name":"Domain Knowledge","url":"https://www.academia.edu/Documents/in/Domain_Knowledge?f_ri=1432"},{"id":255453,"name":"Information Storage and Retrieval","url":"https://www.academia.edu/Documents/in/Information_Storage_and_Retrieval?f_ri=1432"},{"id":271153,"name":"Data representation","url":"https://www.academia.edu/Documents/in/Data_representation?f_ri=1432"},{"id":315668,"name":"Svm","url":"https://www.academia.edu/Documents/in/Svm?f_ri=1432"},{"id":1958410,"name":"Data Grid","url":"https://www.academia.edu/Documents/in/Data_Grid?f_ri=1432"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_29600465" data-work_id="29600465" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/29600465/Prosodic_comparison_of_declarative_and_interrogative_utterances_in_Standard_Colloquial_Bangla">Prosodic comparison of declarative and interrogative utterances in Standard Colloquial Bangla</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">This paper presents a comparative study of prosodic features of utterances of two types of Bangla sentences: the declarative type and the interrogative ('yes-no' question) type whose textual contents are identical except for the... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_29600465" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">This paper presents a comparative study of prosodic features of utterances of two types of Bangla sentences: the declarative type and the interrogative ('yes-no' question) type whose textual contents are identical except for the punctuation marks in Bangla. The study is based on the analysis of 44 utterances each of the declarative type and the interrogative type spoken by native speakers of Standard Colloquial Bangla (SCB). The results of F 0 contour analysis show that declarative utterances have a gradually falling F 0 contour with a terminal fall whereas interrogative utterances have a rising contour and a terminal rise. It is also observed that interrogative utterances have a positive swing of F 0 larger than that of declarative utterances, and the maximum of F 0 occurs within the prosodic word containing the interrogative information. It is shown that these differences can be represented in terms of differences in parameters of the F 0 contour command-response model. The validity of the analysis was confirmed by perceptual experiments using synthetic stimuli, demonstrating the feasibility of the command-response model in speech synthesis of Bangla.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/29600465" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="0abf0ea2e5ebf73e875b6a40453e1264" rel="nofollow" data-download="{"attachment_id":50041246,"asset_id":29600465,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/50041246/download_file?st=MTczOTgzODMzOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="55886757" href="https://independent.academia.edu/FujisakiHiroya">Hiroya Fujisaki</a><script data-card-contents-for-user="55886757" type="text/json">{"id":55886757,"first_name":"Hiroya","last_name":"Fujisaki","domain_name":"independent","page_name":"FujisakiHiroya","display_name":"Hiroya Fujisaki","profile_url":"https://independent.academia.edu/FujisakiHiroya?f_ri=1432","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_29600465 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="29600465"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 29600465, container: ".js-paper-rank-work_29600465", }); 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$(".js-view-count[data-work-id=29600465]").text(description); $(".js-view-count-work_29600465").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_29600465").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="29600465"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">6</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="1432" rel="nofollow" href="https://www.academia.edu/Documents/in/Natural_Language_Processing">Natural Language Processing</a>, <script data-card-contents-for-ri="1432" type="text/json">{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="2342" rel="nofollow" href="https://www.academia.edu/Documents/in/Speech_Synthesis">Speech Synthesis</a>, <script data-card-contents-for-ri="2342" type="text/json">{"id":2342,"name":"Speech Synthesis","url":"https://www.academia.edu/Documents/in/Speech_Synthesis?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="160144" rel="nofollow" href="https://www.academia.edu/Documents/in/Feature_Extraction">Feature Extraction</a>, <script data-card-contents-for-ri="160144" type="text/json">{"id":160144,"name":"Feature Extraction","url":"https://www.academia.edu/Documents/in/Feature_Extraction?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="413300" rel="nofollow" href="https://www.academia.edu/Documents/in/Analytical_Model">Analytical Model</a><script data-card-contents-for-ri="413300" type="text/json">{"id":413300,"name":"Analytical Model","url":"https://www.academia.edu/Documents/in/Analytical_Model?f_ri=1432","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=29600465]'), work: {"id":29600465,"title":"Prosodic comparison of declarative and interrogative utterances in Standard Colloquial Bangla","created_at":"2016-11-01T15:59:24.516-07:00","url":"https://www.academia.edu/29600465/Prosodic_comparison_of_declarative_and_interrogative_utterances_in_Standard_Colloquial_Bangla?f_ri=1432","dom_id":"work_29600465","summary":"This paper presents a comparative study of prosodic features of utterances of two types of Bangla sentences: the declarative type and the interrogative ('yes-no' question) type whose textual contents are identical except for the punctuation marks in Bangla. The study is based on the analysis of 44 utterances each of the declarative type and the interrogative type spoken by native speakers of Standard Colloquial Bangla (SCB). The results of F 0 contour analysis show that declarative utterances have a gradually falling F 0 contour with a terminal fall whereas interrogative utterances have a rising contour and a terminal rise. It is also observed that interrogative utterances have a positive swing of F 0 larger than that of declarative utterances, and the maximum of F 0 occurs within the prosodic word containing the interrogative information. It is shown that these differences can be represented in terms of differences in parameters of the F 0 contour command-response model. The validity of the analysis was confirmed by perceptual experiments using synthetic stimuli, demonstrating the feasibility of the command-response model in speech synthesis of Bangla.","downloadable_attachments":[{"id":50041246,"asset_id":29600465,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":55886757,"first_name":"Hiroya","last_name":"Fujisaki","domain_name":"independent","page_name":"FujisakiHiroya","display_name":"Hiroya Fujisaki","profile_url":"https://independent.academia.edu/FujisakiHiroya?f_ri=1432","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true},{"id":2342,"name":"Speech Synthesis","url":"https://www.academia.edu/Documents/in/Speech_Synthesis?f_ri=1432","nofollow":true},{"id":160144,"name":"Feature Extraction","url":"https://www.academia.edu/Documents/in/Feature_Extraction?f_ri=1432","nofollow":true},{"id":413300,"name":"Analytical Model","url":"https://www.academia.edu/Documents/in/Analytical_Model?f_ri=1432","nofollow":true},{"id":726215,"name":"Speech database","url":"https://www.academia.edu/Documents/in/Speech_database?f_ri=1432"},{"id":999795,"name":"Native Speaker","url":"https://www.academia.edu/Documents/in/Native_Speaker?f_ri=1432"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_26593227 coauthored" data-work_id="26593227" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/26593227/Text_to_software">Text to software</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Software development relies heavily on manual processes for transforming requirements into software artifacts such as models, source code, or test cases. Requirements are the starting point for these transformations, and they are... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_26593227" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Software development relies heavily on manual processes for transforming requirements into software artifacts such as models, source code, or test cases. Requirements are the starting point for these transformations, and they are typically written in natural language. However, hardly any automated tools exist that translate natural language texts into software artifacts.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/26593227" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="ed119e9dec2c5998bd2837a3598dd0b0" rel="nofollow" data-download="{"attachment_id":46882132,"asset_id":26593227,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/46882132/download_file?st=MTczOTgzODMzOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="50586500" href="https://independent.academia.edu/SvenKoerner">Sven Koerner</a><script data-card-contents-for-user="50586500" type="text/json">{"id":50586500,"first_name":"Sven","last_name":"Koerner","domain_name":"independent","page_name":"SvenKoerner","display_name":"Sven Koerner","profile_url":"https://independent.academia.edu/SvenKoerner?f_ri=1432","photo":"/images/s65_no_pic.png"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-26593227">+1</span><div class="hidden js-additional-users-26593227"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://kit.academia.edu/Tichy">Walter Tichy</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-26593227'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-26593227').html(); 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container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_26593227 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="26593227"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 26593227; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=26593227]").text(description); $(".js-view-count-work_26593227").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_26593227").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="26593227"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">9</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="449" rel="nofollow" href="https://www.academia.edu/Documents/in/Software_Engineering">Software Engineering</a>, <script data-card-contents-for-ri="449" type="text/json">{"id":449,"name":"Software Engineering","url":"https://www.academia.edu/Documents/in/Software_Engineering?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="1432" rel="nofollow" href="https://www.academia.edu/Documents/in/Natural_Language_Processing">Natural Language Processing</a>, <script data-card-contents-for-ri="1432" type="text/json">{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="1449" rel="nofollow" href="https://www.academia.edu/Documents/in/Requirements_Engineering">Requirements Engineering</a>, <script data-card-contents-for-ri="1449" type="text/json">{"id":1449,"name":"Requirements Engineering","url":"https://www.academia.edu/Documents/in/Requirements_Engineering?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="8129" rel="nofollow" href="https://www.academia.edu/Documents/in/Software_Development">Software Development</a><script data-card-contents-for-ri="8129" type="text/json">{"id":8129,"name":"Software Development","url":"https://www.academia.edu/Documents/in/Software_Development?f_ri=1432","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=26593227]'), work: {"id":26593227,"title":"Text to software","created_at":"2016-06-29T03:52:28.660-07:00","url":"https://www.academia.edu/26593227/Text_to_software?f_ri=1432","dom_id":"work_26593227","summary":"Software development relies heavily on manual processes for transforming requirements into software artifacts such as models, source code, or test cases. Requirements are the starting point for these transformations, and they are typically written in natural language. However, hardly any automated tools exist that translate natural language texts into software artifacts.","downloadable_attachments":[{"id":46882132,"asset_id":26593227,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":50586500,"first_name":"Sven","last_name":"Koerner","domain_name":"independent","page_name":"SvenKoerner","display_name":"Sven Koerner","profile_url":"https://independent.academia.edu/SvenKoerner?f_ri=1432","photo":"/images/s65_no_pic.png"},{"id":50575111,"first_name":"Walter","last_name":"Tichy","domain_name":"kit","page_name":"Tichy","display_name":"Walter Tichy","profile_url":"https://kit.academia.edu/Tichy?f_ri=1432","photo":"https://0.academia-photos.com/50575111/14316882/15256996/s65_walter.tichy.jpg"}],"research_interests":[{"id":449,"name":"Software Engineering","url":"https://www.academia.edu/Documents/in/Software_Engineering?f_ri=1432","nofollow":true},{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true},{"id":1449,"name":"Requirements Engineering","url":"https://www.academia.edu/Documents/in/Requirements_Engineering?f_ri=1432","nofollow":true},{"id":8129,"name":"Software Development","url":"https://www.academia.edu/Documents/in/Software_Development?f_ri=1432","nofollow":true},{"id":66379,"name":"Automation","url":"https://www.academia.edu/Documents/in/Automation?f_ri=1432"},{"id":97618,"name":"Natural language","url":"https://www.academia.edu/Documents/in/Natural_language?f_ri=1432"},{"id":106716,"name":"Requirement Engineering","url":"https://www.academia.edu/Documents/in/Requirement_Engineering?f_ri=1432"},{"id":516232,"name":"Source Code","url":"https://www.academia.edu/Documents/in/Source_Code?f_ri=1432"},{"id":599685,"name":"Semantic technologies","url":"https://www.academia.edu/Documents/in/Semantic_technologies?f_ri=1432"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_23456160" data-work_id="23456160" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/23456160/A_Pattern_Dictionary_for_Natural_Language_Processing">A Pattern Dictionary for Natural Language Processing</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Abstract: This paper briefly surveys three of the main resources for word sense disambiguation that are currently in use WordNet, FrameNet, and Levin classes and proposes an alternative approach, focusing on verbs and their valencies.... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_23456160" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Abstract: This paper briefly surveys three of the main resources for word sense disambiguation that are currently in use WordNet, FrameNet, and Levin classes and proposes an alternative approach, focusing on verbs and their valencies. This new approach does not attempt to ...</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/23456160" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="6b131ab4ce7b1872a5b43e8f52e39f8b" rel="nofollow" data-download="{"attachment_id":43895662,"asset_id":23456160,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/43895662/download_file?st=MTczOTgzODMzOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="5665763" href="https://independent.academia.edu/JamesPustejovsky">James PUSTEJOVSKY</a><script data-card-contents-for-user="5665763" type="text/json">{"id":5665763,"first_name":"James","last_name":"PUSTEJOVSKY","domain_name":"independent","page_name":"JamesPustejovsky","display_name":"James PUSTEJOVSKY","profile_url":"https://independent.academia.edu/JamesPustejovsky?f_ri=1432","photo":"https://0.academia-photos.com/5665763/3270500/149787509/s65_james.pustejovsky.jpg"}</script></span></span></li><li class="js-paper-rank-work_23456160 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="23456160"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 23456160, container: ".js-paper-rank-work_23456160", }); 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$(".js-view-count[data-work-id=23456160]").text(description); $(".js-view-count-work_23456160").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_23456160").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="23456160"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">2</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="1432" rel="nofollow" href="https://www.academia.edu/Documents/in/Natural_Language_Processing">Natural Language Processing</a>, <script data-card-contents-for-ri="1432" type="text/json">{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="3268" rel="nofollow" href="https://www.academia.edu/Documents/in/Computational_Linguistics">Computational Linguistics</a><script data-card-contents-for-ri="3268" type="text/json">{"id":3268,"name":"Computational Linguistics","url":"https://www.academia.edu/Documents/in/Computational_Linguistics?f_ri=1432","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=23456160]'), work: {"id":23456160,"title":"A Pattern Dictionary for Natural Language Processing","created_at":"2016-03-19T10:42:51.841-07:00","url":"https://www.academia.edu/23456160/A_Pattern_Dictionary_for_Natural_Language_Processing?f_ri=1432","dom_id":"work_23456160","summary":"Abstract: This paper briefly surveys three of the main resources for word sense disambiguation that are currently in use WordNet, FrameNet, and Levin classes and proposes an alternative approach, focusing on verbs and their valencies. This new approach does not attempt to ...","downloadable_attachments":[{"id":43895662,"asset_id":23456160,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":5665763,"first_name":"James","last_name":"PUSTEJOVSKY","domain_name":"independent","page_name":"JamesPustejovsky","display_name":"James PUSTEJOVSKY","profile_url":"https://independent.academia.edu/JamesPustejovsky?f_ri=1432","photo":"https://0.academia-photos.com/5665763/3270500/149787509/s65_james.pustejovsky.jpg"}],"research_interests":[{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true},{"id":3268,"name":"Computational Linguistics","url":"https://www.academia.edu/Documents/in/Computational_Linguistics?f_ri=1432","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_73734688" data-work_id="73734688" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/73734688/Feature_Engineering_for_Text_Classification">Feature Engineering for Text Classification</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Most research in text classification to date has used a bag of words representation in which each feature corresponds to a single word. This paper examines some alternative ways to represent text based on syntactic and semantic... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_73734688" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Most research in text classification to date has used a bag of words representation in which each feature corresponds to a single word. This paper examines some alternative ways to represent text based on syntactic and semantic relationships between words (phrases, synonyms ...</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/73734688" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="68752531fd615cbe74741556cbb670e5" rel="nofollow" data-download="{"attachment_id":82139517,"asset_id":73734688,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/82139517/download_file?st=MTczOTgzODMzOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="97032717" href="https://independent.academia.edu/SamScott39">Sam Scott</a><script data-card-contents-for-user="97032717" type="text/json">{"id":97032717,"first_name":"Sam","last_name":"Scott","domain_name":"independent","page_name":"SamScott39","display_name":"Sam Scott","profile_url":"https://independent.academia.edu/SamScott39?f_ri=1432","photo":"https://0.academia-photos.com/97032717/28091144/26321233/s65_sam.scott.jpg"}</script></span></span></li><li class="js-paper-rank-work_73734688 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="73734688"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 73734688, container: ".js-paper-rank-work_73734688", }); 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$(".js-view-count[data-work-id=73734688]").text(description); $(".js-view-count-work_73734688").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_73734688").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="73734688"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">17</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="1432" rel="nofollow" href="https://www.academia.edu/Documents/in/Natural_Language_Processing">Natural Language Processing</a>, <script data-card-contents-for-ri="1432" type="text/json">{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="9486" rel="nofollow" href="https://www.academia.edu/Documents/in/Representations">Representations</a>, <script data-card-contents-for-ri="9486" type="text/json">{"id":9486,"name":"Representations","url":"https://www.academia.edu/Documents/in/Representations?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="43774" rel="nofollow" href="https://www.academia.edu/Documents/in/Learning">Learning</a>, <script data-card-contents-for-ri="43774" type="text/json">{"id":43774,"name":"Learning","url":"https://www.academia.edu/Documents/in/Learning?f_ri=1432","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="51829" rel="nofollow" href="https://www.academia.edu/Documents/in/Text_Classification">Text Classification</a><script data-card-contents-for-ri="51829" type="text/json">{"id":51829,"name":"Text Classification","url":"https://www.academia.edu/Documents/in/Text_Classification?f_ri=1432","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=73734688]'), work: {"id":73734688,"title":"Feature Engineering for Text Classification","created_at":"2022-03-14T05:03:51.271-07:00","url":"https://www.academia.edu/73734688/Feature_Engineering_for_Text_Classification?f_ri=1432","dom_id":"work_73734688","summary":"Most research in text classification to date has used a bag of words representation in which each feature corresponds to a single word. This paper examines some alternative ways to represent text based on syntactic and semantic relationships between words (phrases, synonyms ...","downloadable_attachments":[{"id":82139517,"asset_id":73734688,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":97032717,"first_name":"Sam","last_name":"Scott","domain_name":"independent","page_name":"SamScott39","display_name":"Sam Scott","profile_url":"https://independent.academia.edu/SamScott39?f_ri=1432","photo":"https://0.academia-photos.com/97032717/28091144/26321233/s65_sam.scott.jpg"}],"research_interests":[{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true},{"id":9486,"name":"Representations","url":"https://www.academia.edu/Documents/in/Representations?f_ri=1432","nofollow":true},{"id":43774,"name":"Learning","url":"https://www.academia.edu/Documents/in/Learning?f_ri=1432","nofollow":true},{"id":51829,"name":"Text Classification","url":"https://www.academia.edu/Documents/in/Text_Classification?f_ri=1432","nofollow":true},{"id":73048,"name":"Learning Algorithms","url":"https://www.academia.edu/Documents/in/Learning_Algorithms?f_ri=1432"},{"id":87507,"name":"Wordnet","url":"https://www.academia.edu/Documents/in/Wordnet?f_ri=1432"},{"id":106145,"name":"Classification","url":"https://www.academia.edu/Documents/in/Classification?f_ri=1432"},{"id":137957,"name":"Performance Improvement","url":"https://www.academia.edu/Documents/in/Performance_Improvement?f_ri=1432"},{"id":144969,"name":"Phrases","url":"https://www.academia.edu/Documents/in/Phrases?f_ri=1432"},{"id":473592,"name":"Feature","url":"https://www.academia.edu/Documents/in/Feature?f_ri=1432"},{"id":516448,"name":"Test Collection","url":"https://www.academia.edu/Documents/in/Test_Collection?f_ri=1432"},{"id":1180378,"name":"Synonyms","url":"https://www.academia.edu/Documents/in/Synonyms?f_ri=1432"},{"id":1216932,"name":"Rule Based","url":"https://www.academia.edu/Documents/in/Rule_Based?f_ri=1432"},{"id":1384176,"name":"Feature Engineering","url":"https://www.academia.edu/Documents/in/Feature_Engineering?f_ri=1432"},{"id":2821309,"name":"learning algorithm","url":"https://www.academia.edu/Documents/in/learning_algorithm?f_ri=1432"},{"id":3890758,"name":"Majority voting","url":"https://www.academia.edu/Documents/in/Majority_voting?f_ri=1432"},{"id":4037593,"name":"Ripper","url":"https://www.academia.edu/Documents/in/Ripper?f_ri=1432"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_12111943" data-work_id="12111943" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/12111943/Chinese_Irony_Corpus_Construction_and_Ironic_Structure_Analysis">Chinese Irony Corpus Construction and Ironic Structure Analysis</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Non-literal expression recognition is a challenging task in natural language processing. An ironic expression implies the opposite of the literal meaning, causing problems in opinion mining and sentiment analysis. In this paper, ironic... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_12111943" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Non-literal expression recognition is a challenging task in natural language processing. An ironic expression implies the opposite of the literal meaning, causing problems in opinion mining and sentiment analysis. In this paper, ironic messages are collected from microblogs to form an irony corpus based on the use of emoticons, linguistic forms, and sentiment polarity. Five linguistic patterns are mined by using the proposed bootstrapping approach. We also analyze the linguistic structure and elements used to convey irony. Based on our observations, ironic words/phrases and contextual information are the necessary elements in irony, while the contextual information can be hidden in linguistic forms. A rhetorical element, which is optional in irony, can also be used to help strengthen the effects and understandability of an ironic expression. The ironic elements in each instance of our irony corpus are labelled based on this structure. This corpus can be used to study the usage of ironic expressions and the identification of ironic elements, and thus improve the performance of irony recognition. This work is licensed under a Creative Commons Attribution 4.0 International License. Page numbers and proceedings footer are added by the organizers. 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An ironic expression implies the opposite of the literal meaning, causing problems in opinion mining and sentiment analysis. In this paper, ironic messages are collected from microblogs to form an irony corpus based on the use of emoticons, linguistic forms, and sentiment polarity. Five linguistic patterns are mined by using the proposed bootstrapping approach. We also analyze the linguistic structure and elements used to convey irony. Based on our observations, ironic words/phrases and contextual information are the necessary elements in irony, while the contextual information can be hidden in linguistic forms. A rhetorical element, which is optional in irony, can also be used to help strengthen the effects and understandability of an ironic expression. The ironic elements in each instance of our irony corpus are labelled based on this structure. This corpus can be used to study the usage of ironic expressions and the identification of ironic elements, and thus improve the performance of irony recognition. This work is licensed under a Creative Commons Attribution 4.0 International License. Page numbers and proceedings footer are added by the organizers. License details: http:// creativecommons.org/licenses/by/4.0/","downloadable_attachments":[{"id":37425666,"asset_id":12111943,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":30311997,"first_name":"Yi-jie","last_name":"Tang","domain_name":"taiwan","page_name":"YijieTang","display_name":"Yi-jie Tang","profile_url":"https://taiwan.academia.edu/YijieTang?f_ri=1432","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":1200,"name":"Languages and Linguistics","url":"https://www.academia.edu/Documents/in/Languages_and_Linguistics?f_ri=1432","nofollow":true},{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true},{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=1432","nofollow":true},{"id":15674,"name":"Linguistics","url":"https://www.academia.edu/Documents/in/Linguistics?f_ri=1432","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_43727040" data-work_id="43727040" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/43727040/Improving_accuracy_of_part_of_speech_POS_tagging_using_hidden_markov_model_and_morphological_analysis_for_Myanmar_Language_Dim_Lam_Cing_Khin_Mar_Soe">Improving accuracy of part-of-speech (POS) tagging using hidden markov model and morphological analysis for Myanmar Language Dim Lam Cing, Khin Mar Soe</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">In Natural Language Processing (NLP), Word segmentation and Part-of-Speech (POS) tagging are fundamental tasks. The POS information is also necessary in NLP's preprocessing work applications such as machine translation (MT), information... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_43727040" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">In Natural Language Processing (NLP), Word segmentation and Part-of-Speech (POS) tagging are fundamental tasks. The POS information is also necessary in NLP's preprocessing work applications such as machine translation (MT), information retrieval (IR), etc. Currently, there are many research efforts in word segmentation and POS tagging developed separately with different methods to get high performance and accuracy. For Myanmar Language, there are also separate word segmentors and POS taggers based on statistical approaches such as Neural Network (NN) and Hidden Markov Models (HMMs). But, as the Myanmar language's complex morphological structure, the OOV problem still exists. To keep away from error and improve segmentation by utilizing POS data, segmentation and labeling should be possible at the same time.The main goal of developing POS tagger for any Language is to improve accuracy of tagging and remove ambiguity in sentences due to language structure. This paper focuses on developing word segmentation and Part-of-Speech (POS) Tagger for Myanmar Language. This paper presented the comparison of separate word segmentation and POS tagging with joint word segmentation and POS tagging.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/43727040" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="01f2cf51a65f341f709949d669ed0837" rel="nofollow" data-download="{"attachment_id":64035982,"asset_id":43727040,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/64035982/download_file?st=MTczOTgzODMzOSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="163474776" href="https://independent.academia.edu/JournalIJECE">International Journal of Electrical and Computer Engineering (IJECE)</a><script data-card-contents-for-user="163474776" type="text/json">{"id":163474776,"first_name":"International Journal of Electrical and Computer Engineering","last_name":"(IJECE)","domain_name":"independent","page_name":"JournalIJECE","display_name":"International Journal of Electrical and Computer Engineering (IJECE)","profile_url":"https://independent.academia.edu/JournalIJECE?f_ri=1432","photo":"https://0.academia-photos.com/163474776/123357473/112705609/s65_international_journal_of_electrical_and_computer_engineering._ijece_.jpg"}</script></span></span></li><li class="js-paper-rank-work_43727040 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="43727040"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 43727040, container: ".js-paper-rank-work_43727040", }); 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The POS information is also necessary in NLP's preprocessing work applications such as machine translation (MT), information retrieval (IR), etc. Currently, there are many research efforts in word segmentation and POS tagging developed separately with different methods to get high performance and accuracy. For Myanmar Language, there are also separate word segmentors and POS taggers based on statistical approaches such as Neural Network (NN) and Hidden Markov Models (HMMs). But, as the Myanmar language's complex morphological structure, the OOV problem still exists. To keep away from error and improve segmentation by utilizing POS data, segmentation and labeling should be possible at the same time.The main goal of developing POS tagger for any Language is to improve accuracy of tagging and remove ambiguity in sentences due to language structure. This paper focuses on developing word segmentation and Part-of-Speech (POS) Tagger for Myanmar Language. This paper presented the comparison of separate word segmentation and POS tagging with joint word segmentation and POS tagging.","downloadable_attachments":[{"id":64035982,"asset_id":43727040,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":163474776,"first_name":"International Journal of Electrical and Computer Engineering","last_name":"(IJECE)","domain_name":"independent","page_name":"JournalIJECE","display_name":"International Journal of Electrical and Computer Engineering (IJECE)","profile_url":"https://independent.academia.edu/JournalIJECE?f_ri=1432","photo":"https://0.academia-photos.com/163474776/123357473/112705609/s65_international_journal_of_electrical_and_computer_engineering._ijece_.jpg"}],"research_interests":[{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing?f_ri=1432","nofollow":true},{"id":3268,"name":"Computational Linguistics","url":"https://www.academia.edu/Documents/in/Computational_Linguistics?f_ri=1432","nofollow":true},{"id":68937,"name":"Hidden Markov Models","url":"https://www.academia.edu/Documents/in/Hidden_Markov_Models?f_ri=1432","nofollow":true},{"id":149520,"name":"Morphological Analysis","url":"https://www.academia.edu/Documents/in/Morphological_Analysis?f_ri=1432","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div></div><div class="u-taCenter Pagination"><ul class="pagination"><li class="next_page"><a href="/Documents/in/Natural_Language_Processing?after=50%2C43727040" rel="next">Next</a></li><li class="last next"><a href="/Documents/in/Natural_Language_Processing?page=last">Last »</a></li></ul></div></div><div class="hidden-xs hidden-sm"><div class="u-pl6x"><div style="width: 300px;"><div class="panel panel-flat u-mt7x"><div class="panel-heading u-p5x"><div class="u-tcGrayDark u-taCenter u-fw700 u-textUppercase">Related Topics</div></div><ul class="list-group"><li 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