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class="uploads-container" id="social-redesign-work-container"><div class="upload-header"><h2 class="ds2-5-heading-sans-serif-xs">Uploads</h2></div><div class="documents-container backbone-social-profile-documents" style="width: 100%;"><div class="u-taCenter"></div><div class="profile--tab_content_container js-tab-pane tab-pane active" id="all"><div class="profile--tab_heading_container js-section-heading" data-section="Papers" id="Papers"><h3 class="profile--tab_heading_container">Papers by Anupam Mondal</h3></div><div class="js-work-strip profile--work_container" data-work-id="98084110"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/98084110/NTCIR_12_MOBILECLICK_Sense_based_Ranking_and_Summarization_of_English_Queries"><img alt="Research paper thumbnail of NTCIR-12 MOBILECLICK: Sense-based Ranking and Summarization of English Queries" class="work-thumbnail" src="https://attachments.academia-assets.com/99532150/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/98084110/NTCIR_12_MOBILECLICK_Sense_based_Ranking_and_Summarization_of_English_Queries">NTCIR-12 MOBILECLICK: Sense-based Ranking and Summarization of English Queries</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">NTCIR-12 MobileClick task has been designed to rank and summarize English queries. The primary ai...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">NTCIR-12 MobileClick task has been designed to rank and summarize English queries. The primary aim of this task was to develop a system which is capable of minimizing interaction between the human users and mobile phones while extracting relevant data with respect to given queries. Organizers provided the data represented as information units (iUnits). Each of the iUnits describes a pertinent query associated with other information like type or category, relevance, sense and knowledge-based relations [1] [2] [4]. The task is divided into two sub-tasks namely ranking and summarization. The ranking sub-task focuses on identifying the important iUnits related to a query. In the summarization sub-task, the output has to be designed as a two-layered model where the first layer will identify the important iUnits and the second layer will compile those important iUnits and generate a summarized output for the query. In this present task, we have employed several sentiment lexicons like Sen...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e8be735ec68c41bb22f833d2502c3a8c" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":99532150,"asset_id":98084110,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/99532150/download_file?st=MTczMzAzNTQ0OSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="98084110"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="98084110"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 98084110; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=98084110]").text(description); $(".js-view-count[data-work-id=98084110]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 98084110; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='98084110']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 98084110, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "e8be735ec68c41bb22f833d2502c3a8c" } } $('.js-work-strip[data-work-id=98084110]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":98084110,"title":"NTCIR-12 MOBILECLICK: Sense-based Ranking and Summarization of English Queries","translated_title":"","metadata":{"abstract":"NTCIR-12 MobileClick task has been designed to rank and summarize English queries. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="91474039"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/91474039/A_unique_approach_for_Market_Basket_Analysis_under_the_framework_of_Probability_Based"><img alt="Research paper thumbnail of A unique approach for Market Basket Analysis under the framework of Probability Based" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/91474039/A_unique_approach_for_Market_Basket_Analysis_under_the_framework_of_Probability_Based">A unique approach for Market Basket Analysis under the framework of Probability Based</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In recent times, Data Mining plays one of the decisive role in business intelligence. Analyzing e...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In recent times, Data Mining plays one of the decisive role in business intelligence. Analyzing enormous amount of business transaction data is the order of the hour. Association rule learning is one of the major part in Data Mining that helps us to attain functional patterns understanding buying habits which can help in business decision making, increasing revenues, cutting cost etc. Apriori algorithm is one of the well-researched measure to generate association rules which are related set of data existing in transaction data. In this paper, we present an enhanced approach of market basket analysis under the framework of improved probability based Association Rule learning using the notion of Apriori Algorithm. This algorithm can be effectively used in any number of data in the field of continuous production, web usage mining, bioinformatics etc. Data mining refers to the mining of new information in terms of patterns or rules from massive amount of data. Successful organizations v...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="91474039"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="91474039"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 91474039; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=91474039]").text(description); $(".js-view-count[data-work-id=91474039]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 91474039; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='91474039']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 91474039, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=91474039]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":91474039,"title":"A unique approach for Market Basket Analysis under the framework of Probability Based","translated_title":"","metadata":{"abstract":"In recent times, Data Mining plays one of the decisive role in business intelligence. Analyzing enormous amount of business transaction data is the order of the hour. Association rule learning is one of the major part in Data Mining that helps us to attain functional patterns understanding buying habits which can help in business decision making, increasing revenues, cutting cost etc. Apriori algorithm is one of the well-researched measure to generate association rules which are related set of data existing in transaction data. In this paper, we present an enhanced approach of market basket analysis under the framework of improved probability based Association Rule learning using the notion of Apriori Algorithm. This algorithm can be effectively used in any number of data in the field of continuous production, web usage mining, bioinformatics etc. Data mining refers to the mining of new information in terms of patterns or rules from massive amount of data. 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This algorithm can be effectively used in any number of data in the field of continuous production, web usage mining, bioinformatics etc. Data mining refers to the mining of new information in terms of patterns or rules from massive amount of data. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="91474037"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/91474037/An_Annotation_System_to_Annotate_Healthcare_Information_from_Tweets"><img alt="Research paper thumbnail of An Annotation System to Annotate Healthcare Information from Tweets" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/91474037/An_Annotation_System_to_Annotate_Healthcare_Information_from_Tweets">An Annotation System to Annotate Healthcare Information from Tweets</a></div><div class="wp-workCard_item"><span>Advances in Intelligent Systems and Computing</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper presents a unique idea to utilize social media data for the betterment of healthcare s...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This paper presents a unique idea to utilize social media data for the betterment of healthcare service, provided by the doctors and related industries. The researchers have observed that every day, on average, around 5 million tweets are tweeted on Twitter and around 2 billion tweets per year related to health care. This huge data source can be used and analyzed to obtain better knowledge about recent trends and discoveries in a particular field. Hence, we are motivated to develop a structured corpus from scratch, identify concepts and categories using the machine learning approach on the extracted unstructured and semi-structured corpora. In order to build the system, we have employed two well-known classifiers, namely multinomial Naive Bayes and support vector machine on the top of our prepared experimental dataset. The training and test datasets are part of the experimental dataset and have been used to build the module and validate them, respectively. The proposed module is abl...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="91474037"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="91474037"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 91474037; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=91474037]").text(description); $(".js-view-count[data-work-id=91474037]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 91474037; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='91474037']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 91474037, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=91474037]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":91474037,"title":"An Annotation System to Annotate Healthcare Information from Tweets","translated_title":"","metadata":{"abstract":"This paper presents a unique idea to utilize social media data for the betterment of healthcare service, provided by the doctors and related industries. The researchers have observed that every day, on average, around 5 million tweets are tweeted on Twitter and around 2 billion tweets per year related to health care. This huge data source can be used and analyzed to obtain better knowledge about recent trends and discoveries in a particular field. Hence, we are motivated to develop a structured corpus from scratch, identify concepts and categories using the machine learning approach on the extracted unstructured and semi-structured corpora. In order to build the system, we have employed two well-known classifiers, namely multinomial Naive Bayes and support vector machine on the top of our prepared experimental dataset. The training and test datasets are part of the experimental dataset and have been used to build the module and validate them, respectively. The proposed module is abl...","publisher":"Advances in Intelligent Systems and Computing","publication_date":{"day":null,"month":null,"year":2019,"errors":{}},"publication_name":"Advances in Intelligent Systems and Computing"},"translated_abstract":"This paper presents a unique idea to utilize social media data for the betterment of healthcare service, provided by the doctors and related industries. The researchers have observed that every day, on average, around 5 million tweets are tweeted on Twitter and around 2 billion tweets per year related to health care. This huge data source can be used and analyzed to obtain better knowledge about recent trends and discoveries in a particular field. Hence, we are motivated to develop a structured corpus from scratch, identify concepts and categories using the machine learning approach on the extracted unstructured and semi-structured corpora. In order to build the system, we have employed two well-known classifiers, namely multinomial Naive Bayes and support vector machine on the top of our prepared experimental dataset. The training and test datasets are part of the experimental dataset and have been used to build the module and validate them, respectively. The proposed module is abl...","internal_url":"https://www.academia.edu/91474037/An_Annotation_System_to_Annotate_Healthcare_Information_from_Tweets","translated_internal_url":"","created_at":"2022-11-23T16:34:53.968-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":11565886,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"An_Annotation_System_to_Annotate_Healthcare_Information_from_Tweets","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":11565886,"first_name":"Anupam","middle_initials":"","last_name":"Mondal","page_name":"AnupamMondal2","domain_name":"independent","created_at":"2014-04-27T21:03:49.131-07:00","display_name":"Anupam Mondal","url":"https://independent.academia.edu/AnupamMondal2"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":9246,"name":"Social Media","url":"https://www.academia.edu/Documents/in/Social_Media"},{"id":18573,"name":"Categorization","url":"https://www.academia.edu/Documents/in/Categorization"},{"id":38072,"name":"Annotation","url":"https://www.academia.edu/Documents/in/Annotation"},{"id":1553450,"name":"Naive Bayes Classifier","url":"https://www.academia.edu/Documents/in/Naive_Bayes_Classifier"},{"id":1725616,"name":"Automatic Summarization","url":"https://www.academia.edu/Documents/in/Automatic_Summarization"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="91474036"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/91474036/WME_3_0_An_Enhanced_and_Validated_Lexicon_of_Medical_Concepts"><img alt="Research paper thumbnail of WME 3.0: An Enhanced and Validated Lexicon of Medical Concepts" class="work-thumbnail" src="https://attachments.academia-assets.com/94751725/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/91474036/WME_3_0_An_Enhanced_and_Validated_Lexicon_of_Medical_Concepts">WME 3.0: An Enhanced and Validated Lexicon of Medical Concepts</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Information extraction in the medical domain is laborious and time-consuming due to the insuffici...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Information extraction in the medical domain is laborious and time-consuming due to the insufficient number of domain-specific lexicons and lack of involvement of domain experts such as doctors and medical practitioners. Thus, in the present work, we are motivated to design a new lexicon, WME 3.0 (WordNet of Medical Events), which contains over 10,000 medical concepts along with their part of speech, gloss (descriptive explanations), polarity score, sentiment, similar sentiment words, category, affinity score and gravity score features. In addition, the manual annotators help to validate the overall as well as individual category level of medical concepts of WME 3.0 using Cohen’s Kappa agreement metric. The agreement score indicates almost correct identification of medical concepts and their assigned features in WME 3.0.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="15674a2fdeb77006ab7492af8175c9ce" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":94751725,"asset_id":91474036,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/94751725/download_file?st=MTczMzAzNTQ0OSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="91474036"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="91474036"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 91474036; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=91474036]").text(description); $(".js-view-count[data-work-id=91474036]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 91474036; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='91474036']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 91474036, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "15674a2fdeb77006ab7492af8175c9ce" } } $('.js-work-strip[data-work-id=91474036]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":91474036,"title":"WME 3.0: An Enhanced and Validated Lexicon of Medical Concepts","translated_title":"","metadata":{"abstract":"Information extraction in the medical domain is laborious and time-consuming due to the insufficient number of domain-specific lexicons and lack of involvement of domain experts such as doctors and medical practitioners. 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In this paper, we show how we can convert documents into the knowledge of a chatter robot. It helps users to make profit out of it by asking and answering queries, using electronic documents which are integrated with the simulated system. Hence, we are motivated to develop an educational chatbot system which provides a virtual assistant. Its main function is to streamline and to automate manual and administrative tasks while supporting other course-related activities. The aim of this research work is to develop a system which is automated and can provide an answer to a question asked by a user on behalf of a person, for educational purposes. We have focused on the local as well as Web databases to make the model user-friendly, interactive, and scalable.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="91474035"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="91474035"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 91474035; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=91474035]").text(description); $(".js-view-count[data-work-id=91474035]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 91474035; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='91474035']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 91474035, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=91474035]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":91474035,"title":"An Educational Chatbot for Answering Queries","translated_title":"","metadata":{"abstract":"The fast progress in the development of communication and information has made people very diverse in knowledge improvement, education, and learning methods. 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An article in...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Speech and textual information play a crucial role in communicating between humans. An article in “The New York Times” published that now-a-days the adults are spending more than 8 hours a day on screens of computers or mobiles. So the major communication between humans is conducted through web applications such as WhatsApp, Facebook, and Twitter etc as a form of speech and textual conversation. In the present paper, we have focused on designing a textual communication application namely chatbot in the educational domain. The proposed chatbot assists in answering questions provided by the users. To develop the system, we have employed an ensemble learning method as random forest in the presence of extracted features from our prepared dataset. Besides, the validation system offers an average F-measure 0.870 score on various K-values under random forest for the proposed chatbot. Finally, we have deployed the proposed system in a from of telegram bot.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="91474034"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="91474034"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 91474034; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=91474034]").text(description); $(".js-view-count[data-work-id=91474034]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 91474034; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='91474034']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 91474034, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=91474034]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":91474034,"title":"Chatbot: An automated conversation system for the educational domain","translated_title":"","metadata":{"abstract":"Speech and textual information play a crucial role in communicating between humans. 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To develop the system, we have employed an ensemble learning method as random forest in the presence of extracted features from our prepared dataset. Besides, the validation system offers an average F-measure 0.870 score on various K-values under random forest for the proposed chatbot. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="91474033"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/91474033/Relation_Extraction_of_Medical_Concepts_Using_Categorization_and_Sentiment_Analysis"><img alt="Research paper thumbnail of Relation Extraction of Medical Concepts Using Categorization and Sentiment Analysis" class="work-thumbnail" src="https://attachments.academia-assets.com/94751726/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/91474033/Relation_Extraction_of_Medical_Concepts_Using_Categorization_and_Sentiment_Analysis">Relation Extraction of Medical Concepts Using Categorization and Sentiment Analysis</a></div><div class="wp-workCard_item"><span>Cognitive Computation</span><span>, 2018</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="df8083a5dcc05ccde48a5083ced4184f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":94751726,"asset_id":91474033,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/94751726/download_file?st=MTczMzAzNTQ0OSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="91474033"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="91474033"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 91474033; 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The process becomes laborious when the annotation is done manually for the availability of a large number of text corpora. Hence, future automated extraction systems will be essential for groups of experts such as doctors and medical practitioners as well as nonexperts such as patients, to ensure enhanced clinical decision-making for improving healthcare systems. Such extraction systems can be developed using medical concepts and concept-related features as the part of a structured corpus. The latter can assist in assigning the category and sentiment to each of the medical concepts and their lexical contexts. These categories and sentiment assignments constitute semantic relations of medical concepts, with their context, represented by sentences of the corpus. This paper presents a new domain-based knowledge lexicon coupled with a machine learning approach to extract semantic relations. This is done by assigning category and sentiment of the medical concepts and contexts. The categories considered in this research, are diseases, symptoms, drugs, human anatomy, and miscellaneous medical terms, whereas sentiments are considered as positive and negative. The proposed assignment systems are developed on the top of WordNet of Medical Event (WME) lexicon. The developed lexicon provides medical concepts and their features, namely Parts-Of-Speech (POS), gloss (descriptive explanation), Similar Sentiment Words (SSW), affinity score, gravity score, polarity score, and sentiment. Several well-known supervised classifiers, including Naïve Bayes, Logistic Regression, and support vector-based Sequential Minimal Optimization (SMO) have been applied to evaluate the developed systems. The proposed approaches have resulted in a concepts clustering application by identifying the semantic relations of concepts. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="91474015"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/91474015/Relationship_Extraction_based_on_Category_of_Medical_Concepts_from_Lexical_Contexts"><img alt="Research paper thumbnail of Relationship Extraction based on Category of Medical Concepts from Lexical Contexts" class="work-thumbnail" src="https://attachments.academia-assets.com/94751696/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/91474015/Relationship_Extraction_based_on_Category_of_Medical_Concepts_from_Lexical_Contexts">Relationship Extraction based on Category of Medical Concepts from Lexical Contexts</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Medical information extraction is an emerging task in healthcare services aim to acquire crucial ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Medical information extraction is an emerging task in healthcare services aim to acquire crucial information of the concepts like diseases, symptoms, and drugs and also to identify their relations from corpora. In the present article, we have proposed a relationship extraction system based on such categories of medical concepts. We have employed rulebased as well as Support Vector Machine (SVM) based feature-oriented approach along with a domain-specific lexicon viz WordNet of Medical Event (WME 2.0). The lexicon assists in recognizing medical concepts and their related features like Parts-Of-Speech (POS), categories, and Similar Sentiment Words (SSW). We have opted only four fundamental categories diseases, drugs, symptoms, and human anatomy of medical concepts as provided in WME lexicon. Such categories play a crucial role in identifying eight types of different semantic relations viz. drug-drug, disease-drug, and human anatomy-symptom from the medical context. Thereafter, we have...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="be083a45d44d14c2b29c086713cd9e71" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":94751696,"asset_id":91474015,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/94751696/download_file?st=MTczMzAzNTQ1MCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="91474015"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="91474015"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 91474015; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=91474015]").text(description); $(".js-view-count[data-work-id=91474015]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 91474015; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='91474015']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 91474015, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "be083a45d44d14c2b29c086713cd9e71" } } $('.js-work-strip[data-work-id=91474015]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":91474015,"title":"Relationship Extraction based on Category of Medical Concepts from Lexical Contexts","translated_title":"","metadata":{"abstract":"Medical information extraction is an emerging task in healthcare services aim to acquire crucial information of the concepts like diseases, symptoms, and drugs and also to identify their relations from corpora. In the present article, we have proposed a relationship extraction system based on such categories of medical concepts. We have employed rulebased as well as Support Vector Machine (SVM) based feature-oriented approach along with a domain-specific lexicon viz WordNet of Medical Event (WME 2.0). The lexicon assists in recognizing medical concepts and their related features like Parts-Of-Speech (POS), categories, and Similar Sentiment Words (SSW). We have opted only four fundamental categories diseases, drugs, symptoms, and human anatomy of medical concepts as provided in WME lexicon. Such categories play a crucial role in identifying eight types of different semantic relations viz. drug-drug, disease-drug, and human anatomy-symptom from the medical context. Thereafter, we have...","publisher":"ICON","publication_date":{"day":null,"month":null,"year":2017,"errors":{}}},"translated_abstract":"Medical information extraction is an emerging task in healthcare services aim to acquire crucial information of the concepts like diseases, symptoms, and drugs and also to identify their relations from corpora. In the present article, we have proposed a relationship extraction system based on such categories of medical concepts. We have employed rulebased as well as Support Vector Machine (SVM) based feature-oriented approach along with a domain-specific lexicon viz WordNet of Medical Event (WME 2.0). The lexicon assists in recognizing medical concepts and their related features like Parts-Of-Speech (POS), categories, and Similar Sentiment Words (SSW). We have opted only four fundamental categories diseases, drugs, symptoms, and human anatomy of medical concepts as provided in WME lexicon. Such categories play a crucial role in identifying eight types of different semantic relations viz. drug-drug, disease-drug, and human anatomy-symptom from the medical context. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="84506515"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/84506515/Ensemble_approach_for_identifying_medical_concepts_with_special_attention_to_lexical_scope"><img alt="Research paper thumbnail of Ensemble approach for identifying medical concepts with special attention to lexical scope" class="work-thumbnail" src="https://attachments.academia-assets.com/89505492/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/84506515/Ensemble_approach_for_identifying_medical_concepts_with_special_attention_to_lexical_scope">Ensemble approach for identifying medical concepts with special attention to lexical scope</a></div><div class="wp-workCard_item"><span>Sādhanā</span><span>, 2021</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="235b65987a55c1296840a2d44719cda8" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":89505492,"asset_id":84506515,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/89505492/download_file?st=MTczMzAzNTQ1MCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="84506515"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="84506515"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 84506515; 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This extraction process is laborious and time-consuming due to unavailability of medical experts. Thus, in the present task, we were motivated to develop an automated extraction system for identifying medical and non-medical concepts. These concepts help to extract the key information from medical corpora. Not only medical concepts but also their non-medical counterparts are equally important for diagnosis purposes. Hence, we have employed three different approaches such as unsupervised, supervised, and their combined ensemble version to identify both medical and non-medical terms (words/phrases). The unsupervised module consists of two phases: parts-ofspeech (POS) tagging followed by searching in a domain-specific lexicon, namely WordNet of Medical Event (WME 3.0). On the other hand the supervised module is designed by two machine learning classifiers, namely Naïve Bayes and Conditional Random Field (CRF) along with various features like category, POS, sentiment, etc. Finally, we have combined the important outcomes of unsupervised and supervised modules and developed two versions of ensemble module (Ensemble-I and Ensemble-II). All the modules identify uni-gram, bi-gram, tri-gram, and more than tri-gram medical concepts and separate non-medical words or phrases in a context. In order to evaluate all modules of concept identification system, we have prepared an experimental dataset. It has been split into three parts, namely training, development, and test. We observed that ensemble module provides better output in contrast with individual modules and Ensemble-I outperforms Ensemble-II in identifying medical concepts consisting of all possible n-grams. The result analysis shows that the F-measures of 0.91 and 0.94 have been obtained for identifying medical concepts and non-medical words/phrases using both of the ensemble modules, respectively. The present research reports the initial steps to build an automated concept identification framework in health-care. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="84506514"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/84506514/A_Hybrid_Approach_Based_Sentiment_Extraction_from_Medical_Context"><img alt="Research paper thumbnail of A Hybrid Approach Based Sentiment Extraction from Medical Context" class="work-thumbnail" src="https://attachments.academia-assets.com/89505490/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/84506514/A_Hybrid_Approach_Based_Sentiment_Extraction_from_Medical_Context">A Hybrid Approach Based Sentiment Extraction from Medical Context</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In the domain of Bio medical Natural Language Processing (Bio-NLP), the information extraction an...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In the domain of Bio medical Natural Language Processing (Bio-NLP), the information extraction and context sentiment identification are treated as emerging tasks. Several linguistic features like negation , uni-gram, bi-gram, Part-of-Speech (POS) have been used to extract the medical concepts and their sense-based context level information. Thus, in the present attempt, a hybrid approach which is the combination of both linguistic and machine learning approaches has been introduced to extract the contextual sense-based information from a medical corpus. The extraction of sentiment oriented keywords is the crucial part towards identifying the senses of medical contexts. In our previous work, we have developed a medical sense-based lexicon known as WordNet of Medical Event (WME). Several sentiment lexicons like Senti-WordNet, SenticNet etc. were used to represent WME. In contrast, one of our primary motivations here is to build a sentiment extraction model based on medical contexts to...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="4ea5a97dd0bd0573cbeb0933d32e41c4" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":89505490,"asset_id":84506514,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/89505490/download_file?st=MTczMzAzNTQ1MCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="84506514"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="84506514"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 84506514; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=84506514]").text(description); $(".js-view-count[data-work-id=84506514]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 84506514; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='84506514']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 84506514, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "4ea5a97dd0bd0573cbeb0933d32e41c4" } } $('.js-work-strip[data-work-id=84506514]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":84506514,"title":"A Hybrid Approach Based Sentiment Extraction from Medical Context","translated_title":"","metadata":{"abstract":"In the domain of Bio medical Natural Language Processing (Bio-NLP), the information extraction and context sentiment identification are treated as emerging tasks. Several linguistic features like negation , uni-gram, bi-gram, Part-of-Speech (POS) have been used to extract the medical concepts and their sense-based context level information. Thus, in the present attempt, a hybrid approach which is the combination of both linguistic and machine learning approaches has been introduced to extract the contextual sense-based information from a medical corpus. The extraction of sentiment oriented keywords is the crucial part towards identifying the senses of medical contexts. In our previous work, we have developed a medical sense-based lexicon known as WordNet of Medical Event (WME). Several sentiment lexicons like Senti-WordNet, SenticNet etc. were used to represent WME. In contrast, one of our primary motivations here is to build a sentiment extraction model based on medical contexts to...","publisher":"SAAIP@IJCAI","publication_date":{"day":null,"month":null,"year":2016,"errors":{}}},"translated_abstract":"In the domain of Bio medical Natural Language Processing (Bio-NLP), the information extraction and context sentiment identification are treated as emerging tasks. Several linguistic features like negation , uni-gram, bi-gram, Part-of-Speech (POS) have been used to extract the medical concepts and their sense-based context level information. Thus, in the present attempt, a hybrid approach which is the combination of both linguistic and machine learning approaches has been introduced to extract the contextual sense-based information from a medical corpus. The extraction of sentiment oriented keywords is the crucial part towards identifying the senses of medical contexts. In our previous work, we have developed a medical sense-based lexicon known as WordNet of Medical Event (WME). Several sentiment lexicons like Senti-WordNet, SenticNet etc. were used to represent WME. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="84506513"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/84506513/Tracing_Linguistic_Relations_in_Winning_and_Losing_Sides_of_Explicit_Opposing_Groups"><img alt="Research paper thumbnail of Tracing Linguistic Relations in Winning and Losing Sides of Explicit Opposing Groups" class="work-thumbnail" src="https://attachments.academia-assets.com/89505489/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/84506513/Tracing_Linguistic_Relations_in_Winning_and_Losing_Sides_of_Explicit_Opposing_Groups">Tracing Linguistic Relations in Winning and Losing Sides of Explicit Opposing Groups</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Linguistic relations in oral conversations present how opinions are constructed and developed in ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Linguistic relations in oral conversations present how opinions are constructed and developed in a restricted time. The relations bond ideas, arguments, thoughts, and feelings, re-shape them during a speech, and finally build knowledge out of all information provided in the conversation. Speakers share a common interest to discuss. It is expected that each speaker&#39;s reply includes duplicated forms of words from previous speakers. However, linguistic adaptation is observed and evolves in a more complex path than just transferring slightly modified versions of common concepts. A conversation aiming a benefit at the end shows an emergent cooperation inducing the adaptation. Not only cooperation, but also competition drives the adaptation or an opposite scenario and one can capture the dynamic process by tracking how the concepts are linguistically linked. To uncover salient complex dynamic events in verbal communications, we attempt to discover self-organized linguistic relations h...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="eccd0341d213687aa55b0f819185a82b" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":89505489,"asset_id":84506513,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/89505489/download_file?st=MTczMzAzNTQ1MCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="84506513"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="84506513"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 84506513; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=84506513]").text(description); $(".js-view-count[data-work-id=84506513]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 84506513; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='84506513']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 84506513, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "eccd0341d213687aa55b0f819185a82b" } } $('.js-work-strip[data-work-id=84506513]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":84506513,"title":"Tracing Linguistic Relations in Winning and Losing Sides of Explicit Opposing Groups","translated_title":"","metadata":{"abstract":"Linguistic relations in oral conversations present how opinions are constructed and developed in a restricted time. 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To uncover salient complex dynamic events in verbal communications, we attempt to discover self-organized linguistic relations h...","publisher":"FLAIRS Conference","publication_date":{"day":null,"month":null,"year":2017,"errors":{}}},"translated_abstract":"Linguistic relations in oral conversations present how opinions are constructed and developed in a restricted time. The relations bond ideas, arguments, thoughts, and feelings, re-shape them during a speech, and finally build knowledge out of all information provided in the conversation. Speakers share a common interest to discuss. It is expected that each speaker\u0026#39;s reply includes duplicated forms of words from previous speakers. However, linguistic adaptation is observed and evolves in a more complex path than just transferring slightly modified versions of common concepts. A conversation aiming a benefit at the end shows an emergent cooperation inducing the adaptation. Not only cooperation, but also competition drives the adaptation or an opposite scenario and one can capture the dynamic process by tracking how the concepts are linguistically linked. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="84506512"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/84506512/Classification_of_COVID19_tweets_using_Machine_Learning_Approaches"><img alt="Research paper thumbnail of Classification of COVID19 tweets using Machine Learning Approaches" class="work-thumbnail" src="https://attachments.academia-assets.com/89505487/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/84506512/Classification_of_COVID19_tweets_using_Machine_Learning_Approaches">Classification of COVID19 tweets using Machine Learning Approaches</a></div><div class="wp-workCard_item"><span>Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task</span><span>, 2021</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="7015d6d3c579fb06b85963f93d5f4a2b" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":89505487,"asset_id":84506512,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/89505487/download_file?st=MTczMzAzNTQ1MCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="84506512"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="84506512"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 84506512; 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The literature describes two machine learning approaches that were used to build a threeclass classification system, that categorizes tweets related to COVID19, into three classes, viz., self-reports, non-personal reports, and literature/news mentions. The steps for preprocessing tweets, feature extraction, and the development of the machine learning models, are described extensively in the documentation. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="84506511"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/84506511/A_Pictorial_Block_Steganography_based_Secure_Algorithm_for_Data_Transfer"><img alt="Research paper thumbnail of A Pictorial Block Steganography based Secure Algorithm for Data Transfer" class="work-thumbnail" src="https://attachments.academia-assets.com/89505488/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/84506511/A_Pictorial_Block_Steganography_based_Secure_Algorithm_for_Data_Transfer">A Pictorial Block Steganography based Secure Algorithm for Data Transfer</a></div><div class="wp-workCard_item"><span>Ijca Special Issue on International Conference on Computing Communication and Sensor Network</span><span>, Apr 3, 2013</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="4a3c2ee9b5882bf19982787769393510" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":89505488,"asset_id":84506511,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/89505488/download_file?st=MTczMzAzNTQ1MCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="84506511"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="84506511"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 84506511; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="84506510"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/84506510/A_Supervised_Framework_for_Classifying_Dependency_Relations_from_Bengali_Shallow_Parsed_Sentences"><img alt="Research paper thumbnail of A Supervised Framework for Classifying Dependency Relations from Bengali Shallow Parsed Sentences" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/84506510/A_Supervised_Framework_for_Classifying_Dependency_Relations_from_Bengali_Shallow_Parsed_Sentences">A Supervised Framework for Classifying Dependency Relations from Bengali Shallow Parsed Sentences</a></div><div class="wp-workCard_item"><span>Lecture Notes in Computer Science</span><span>, 2015</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Natural Language Processing, one of the contemporary research area has adopted parsing technologi...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Natural Language Processing, one of the contemporary research area has adopted parsing technologies for various languages across the world for different objectives. In the present task, a new approach has been introduced for classifying the dependency parsed relations for a morphologically rich and free-phrase-ordered Indian language like Bengali. The pair of dependency parsed relations also referred as kaarakas &#39;cases&#39; are classified based on different features like vibhaktis inflections, Part-of-Speech POS, punctuation, gender, number and post-position. It is observed that the consecutive and non-consecutive occurrences of such relations play a vital role in the classification. We employed three different machine-learning classifiers, namely NaiveBayes, Sequential Minimal Optimization SMO and Conditional Random Field CRF which obtained the average F-Scores of 0.895, 0.869 and 0.697, respectively for classifying relation pairs of three primary kaarakas and one primary vibhakti relation. We have also conducted the error analysis for such primary relations using confusion matrices.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="84506510"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="84506510"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 84506510; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=84506510]").text(description); $(".js-view-count[data-work-id=84506510]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 84506510; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='84506510']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 84506510, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=84506510]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":84506510,"title":"A Supervised Framework for Classifying Dependency Relations from Bengali Shallow Parsed Sentences","translated_title":"","metadata":{"abstract":"Natural Language Processing, one of the contemporary research area has adopted parsing technologies for various languages across the world for different objectives. In the present task, a new approach has been introduced for classifying the dependency parsed relations for a morphologically rich and free-phrase-ordered Indian language like Bengali. The pair of dependency parsed relations also referred as kaarakas \u0026#39;cases\u0026#39; are classified based on different features like vibhaktis inflections, Part-of-Speech POS, punctuation, gender, number and post-position. It is observed that the consecutive and non-consecutive occurrences of such relations play a vital role in the classification. We employed three different machine-learning classifiers, namely NaiveBayes, Sequential Minimal Optimization SMO and Conditional Random Field CRF which obtained the average F-Scores of 0.895, 0.869 and 0.697, respectively for classifying relation pairs of three primary kaarakas and one primary vibhakti relation. We have also conducted the error analysis for such primary relations using confusion matrices.","publication_date":{"day":null,"month":null,"year":2015,"errors":{}},"publication_name":"Lecture Notes in Computer Science"},"translated_abstract":"Natural Language Processing, one of the contemporary research area has adopted parsing technologies for various languages across the world for different objectives. In the present task, a new approach has been introduced for classifying the dependency parsed relations for a morphologically rich and free-phrase-ordered Indian language like Bengali. The pair of dependency parsed relations also referred as kaarakas \u0026#39;cases\u0026#39; are classified based on different features like vibhaktis inflections, Part-of-Speech POS, punctuation, gender, number and post-position. It is observed that the consecutive and non-consecutive occurrences of such relations play a vital role in the classification. We employed three different machine-learning classifiers, namely NaiveBayes, Sequential Minimal Optimization SMO and Conditional Random Field CRF which obtained the average F-Scores of 0.895, 0.869 and 0.697, respectively for classifying relation pairs of three primary kaarakas and one primary vibhakti relation. We have also conducted the error analysis for such primary relations using confusion matrices.","internal_url":"https://www.academia.edu/84506510/A_Supervised_Framework_for_Classifying_Dependency_Relations_from_Bengali_Shallow_Parsed_Sentences","translated_internal_url":"","created_at":"2022-08-11T18:07:26.368-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":11565886,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"A_Supervised_Framework_for_Classifying_Dependency_Relations_from_Bengali_Shallow_Parsed_Sentences","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":11565886,"first_name":"Anupam","middle_initials":"","last_name":"Mondal","page_name":"AnupamMondal2","domain_name":"independent","created_at":"2014-04-27T21:03:49.131-07:00","display_name":"Anupam Mondal","url":"https://independent.academia.edu/AnupamMondal2"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence"},{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="84506499"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/84506499/Auto_categorization_of_medical_concepts_and_contexts"><img alt="Research paper thumbnail of Auto-categorization of medical concepts and contexts" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/84506499/Auto_categorization_of_medical_concepts_and_contexts">Auto-categorization of medical concepts and contexts</a></div><div class="wp-workCard_item"><span>2017 IEEE Symposium Series on Computational Intelligence (SSCI)</span><span>, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In healthcare, information extraction is important in order to identify conceptual knowledge as a...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In healthcare, information extraction is important in order to identify conceptual knowledge as a category of medical concepts from a large number of unstructured and semi-structured corpora. Category describes how medical concepts are fundamentally separated from each other to represent their conceptual knowledge in the corpus. In this paper, we focus on identifying the category of medical concepts and contexts which describe the subjective and the conceptual information of the medical corpus. To recognize the medical concept and assign their category, we employ our previously developed WordNet of Medical Event (WME 2.0) domain-specific lexicon. The lexicon provides medical concepts and their affinity, gravity, polarity scores, similar sentiment words, and sentiment features, help to develop the category assignment system. The identified categories for the concepts are diseases, drugs, symptoms, human_anatomy, and miscellaneous medical terms (MMT), which all refer the broadest fundamental classes of medical concepts. Therefore, the assigned categories of medical concepts used to build the category assignment system for the medical context. The proposed system allows extracting eleven types of pairbased categories as disease-symptom, disease-drug, and disease-MMT of contexts. To validate the categorization system for medical concepts and contexts, we have employed widely used supervised machine learning classifiers namely Naïve Bayes and Logistic Regression in the presence of WME 2.0 lexicon. The two classifiers provide F-scores of 0.81 and 0.86 for the concepts and contexts categorization systems, respectively.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="84506499"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="84506499"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 84506499; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=84506499]").text(description); $(".js-view-count[data-work-id=84506499]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 84506499; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='84506499']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 84506499, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=84506499]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":84506499,"title":"Auto-categorization of medical concepts and contexts","translated_title":"","metadata":{"abstract":"In healthcare, information extraction is important in order to identify conceptual knowledge as a category of medical concepts from a large number of unstructured and semi-structured corpora. Category describes how medical concepts are fundamentally separated from each other to represent their conceptual knowledge in the corpus. In this paper, we focus on identifying the category of medical concepts and contexts which describe the subjective and the conceptual information of the medical corpus. To recognize the medical concept and assign their category, we employ our previously developed WordNet of Medical Event (WME 2.0) domain-specific lexicon. The lexicon provides medical concepts and their affinity, gravity, polarity scores, similar sentiment words, and sentiment features, help to develop the category assignment system. The identified categories for the concepts are diseases, drugs, symptoms, human_anatomy, and miscellaneous medical terms (MMT), which all refer the broadest fundamental classes of medical concepts. Therefore, the assigned categories of medical concepts used to build the category assignment system for the medical context. The proposed system allows extracting eleven types of pairbased categories as disease-symptom, disease-drug, and disease-MMT of contexts. To validate the categorization system for medical concepts and contexts, we have employed widely used supervised machine learning classifiers namely Naïve Bayes and Logistic Regression in the presence of WME 2.0 lexicon. The two classifiers provide F-scores of 0.81 and 0.86 for the concepts and contexts categorization systems, respectively.","publisher":"IEEE","publication_date":{"day":null,"month":null,"year":2017,"errors":{}},"publication_name":"2017 IEEE Symposium Series on Computational Intelligence (SSCI)"},"translated_abstract":"In healthcare, information extraction is important in order to identify conceptual knowledge as a category of medical concepts from a large number of unstructured and semi-structured corpora. Category describes how medical concepts are fundamentally separated from each other to represent their conceptual knowledge in the corpus. In this paper, we focus on identifying the category of medical concepts and contexts which describe the subjective and the conceptual information of the medical corpus. To recognize the medical concept and assign their category, we employ our previously developed WordNet of Medical Event (WME 2.0) domain-specific lexicon. The lexicon provides medical concepts and their affinity, gravity, polarity scores, similar sentiment words, and sentiment features, help to develop the category assignment system. The identified categories for the concepts are diseases, drugs, symptoms, human_anatomy, and miscellaneous medical terms (MMT), which all refer the broadest fundamental classes of medical concepts. Therefore, the assigned categories of medical concepts used to build the category assignment system for the medical context. The proposed system allows extracting eleven types of pairbased categories as disease-symptom, disease-drug, and disease-MMT of contexts. To validate the categorization system for medical concepts and contexts, we have employed widely used supervised machine learning classifiers namely Naïve Bayes and Logistic Regression in the presence of WME 2.0 lexicon. The two classifiers provide F-scores of 0.81 and 0.86 for the concepts and contexts categorization systems, respectively.","internal_url":"https://www.academia.edu/84506499/Auto_categorization_of_medical_concepts_and_contexts","translated_internal_url":"","created_at":"2022-08-11T18:06:42.571-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":11565886,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Auto_categorization_of_medical_concepts_and_contexts","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":11565886,"first_name":"Anupam","middle_initials":"","last_name":"Mondal","page_name":"AnupamMondal2","domain_name":"independent","created_at":"2014-04-27T21:03:49.131-07:00","display_name":"Anupam Mondal","url":"https://independent.academia.edu/AnupamMondal2"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence"},{"id":18573,"name":"Categorization","url":"https://www.academia.edu/Documents/in/Categorization"}],"urls":[{"id":22807558,"url":"http://xplorestaging.ieee.org/ielx7/8267146/8280782/08285253.pdf?arnumber=8285253"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="29412504"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/29412504/Lexical_Resource_for_Medical_Events_A_Polarity_Based_Approach"><img alt="Research paper thumbnail of Lexical Resource for Medical Events: A Polarity Based Approach" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/29412504/Lexical_Resource_for_Medical_Events_A_Polarity_Based_Approach">Lexical Resource for Medical Events: A Polarity Based Approach</a></div><div class="wp-workCard_item"><span>2015 IEEE International Conference on Data Mining Workshop (ICDMW)</span><span>, 2015</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="29412504"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="29412504"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 29412504; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=29412504]").text(description); $(".js-view-count[data-work-id=29412504]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 29412504; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='29412504']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 29412504, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=29412502]").text(description); $(".js-view-count[data-work-id=29412502]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 29412502; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='29412502']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 29412502, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "a619e77760cd9057e34948a5e24dc0f9" } } $('.js-work-strip[data-work-id=29412502]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":29412502,"title":"A Radical Approach for Market Basket Analysis under the Framework of Binary Transaction based improved Apriori Algorithm","translated_title":"","metadata":{"grobid_abstract":"In modern world, analyzing data and extracting useful information from the data is one of the crucial task in business analysis. Now, extracting patterns from the data has occurred from centuries. Bayes theorem (used in the 1700s), Regression analysis (used in the 1800s) were the earlier process of identifying and extracting pattern from a huge collection of relevant data. In this regard, association rule learning is one of the popular, well researched procedures to extract pattern or rules to gather information from huge relevant data. In this paper, a new approach of segregating data and generating rules under the framework of binary transaction based modified enhanced Apriori Algorithm have been presented. This algorithm can be applied in any number of data efficiently. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> </div><div class="profile--tab_content_container js-tab-pane tab-pane" data-section-id="6024550" id="papers"><div class="js-work-strip profile--work_container" data-work-id="98084110"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/98084110/NTCIR_12_MOBILECLICK_Sense_based_Ranking_and_Summarization_of_English_Queries"><img alt="Research paper thumbnail of NTCIR-12 MOBILECLICK: Sense-based Ranking and Summarization of English Queries" class="work-thumbnail" src="https://attachments.academia-assets.com/99532150/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/98084110/NTCIR_12_MOBILECLICK_Sense_based_Ranking_and_Summarization_of_English_Queries">NTCIR-12 MOBILECLICK: Sense-based Ranking and Summarization of English Queries</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">NTCIR-12 MobileClick task has been designed to rank and summarize English queries. The primary ai...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">NTCIR-12 MobileClick task has been designed to rank and summarize English queries. The primary aim of this task was to develop a system which is capable of minimizing interaction between the human users and mobile phones while extracting relevant data with respect to given queries. Organizers provided the data represented as information units (iUnits). Each of the iUnits describes a pertinent query associated with other information like type or category, relevance, sense and knowledge-based relations [1] [2] [4]. The task is divided into two sub-tasks namely ranking and summarization. The ranking sub-task focuses on identifying the important iUnits related to a query. In the summarization sub-task, the output has to be designed as a two-layered model where the first layer will identify the important iUnits and the second layer will compile those important iUnits and generate a summarized output for the query. 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Analyzing e...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In recent times, Data Mining plays one of the decisive role in business intelligence. Analyzing enormous amount of business transaction data is the order of the hour. Association rule learning is one of the major part in Data Mining that helps us to attain functional patterns understanding buying habits which can help in business decision making, increasing revenues, cutting cost etc. Apriori algorithm is one of the well-researched measure to generate association rules which are related set of data existing in transaction data. In this paper, we present an enhanced approach of market basket analysis under the framework of improved probability based Association Rule learning using the notion of Apriori Algorithm. This algorithm can be effectively used in any number of data in the field of continuous production, web usage mining, bioinformatics etc. Data mining refers to the mining of new information in terms of patterns or rules from massive amount of data. Successful organizations v...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="91474039"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="91474039"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 91474039; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=91474039]").text(description); $(".js-view-count[data-work-id=91474039]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 91474039; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='91474039']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 91474039, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=91474039]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":91474039,"title":"A unique approach for Market Basket Analysis under the framework of Probability Based","translated_title":"","metadata":{"abstract":"In recent times, Data Mining plays one of the decisive role in business intelligence. 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This algorithm can be effectively used in any number of data in the field of continuous production, web usage mining, bioinformatics etc. Data mining refers to the mining of new information in terms of patterns or rules from massive amount of data. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="91474037"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/91474037/An_Annotation_System_to_Annotate_Healthcare_Information_from_Tweets"><img alt="Research paper thumbnail of An Annotation System to Annotate Healthcare Information from Tweets" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/91474037/An_Annotation_System_to_Annotate_Healthcare_Information_from_Tweets">An Annotation System to Annotate Healthcare Information from Tweets</a></div><div class="wp-workCard_item"><span>Advances in Intelligent Systems and Computing</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper presents a unique idea to utilize social media data for the betterment of healthcare s...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This paper presents a unique idea to utilize social media data for the betterment of healthcare service, provided by the doctors and related industries. The researchers have observed that every day, on average, around 5 million tweets are tweeted on Twitter and around 2 billion tweets per year related to health care. This huge data source can be used and analyzed to obtain better knowledge about recent trends and discoveries in a particular field. Hence, we are motivated to develop a structured corpus from scratch, identify concepts and categories using the machine learning approach on the extracted unstructured and semi-structured corpora. In order to build the system, we have employed two well-known classifiers, namely multinomial Naive Bayes and support vector machine on the top of our prepared experimental dataset. The training and test datasets are part of the experimental dataset and have been used to build the module and validate them, respectively. The proposed module is abl...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="91474037"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="91474037"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 91474037; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=91474037]").text(description); $(".js-view-count[data-work-id=91474037]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 91474037; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='91474037']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 91474037, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=91474037]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":91474037,"title":"An Annotation System to Annotate Healthcare Information from Tweets","translated_title":"","metadata":{"abstract":"This paper presents a unique idea to utilize social media data for the betterment of healthcare service, provided by the doctors and related industries. 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Thus, in the present work, we are motivated to design a new lexicon, WME 3.0 (WordNet of Medical Events), which contains over 10,000 medical concepts along with their part of speech, gloss (descriptive explanations), polarity score, sentiment, similar sentiment words, category, affinity score and gravity score features. In addition, the manual annotators help to validate the overall as well as individual category level of medical concepts of WME 3.0 using Cohen’s Kappa agreement metric. The agreement score indicates almost correct identification of medical concepts and their assigned features in WME 3.0.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="15674a2fdeb77006ab7492af8175c9ce" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":94751725,"asset_id":91474036,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/94751725/download_file?st=MTczMzAzNTQ1MCw4LjIyMi4yMDguMTQ2&st=MTczMzAzNTQ0OSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="91474036"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="91474036"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 91474036; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=91474036]").text(description); $(".js-view-count[data-work-id=91474036]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 91474036; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='91474036']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 91474036, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "15674a2fdeb77006ab7492af8175c9ce" } } $('.js-work-strip[data-work-id=91474036]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":91474036,"title":"WME 3.0: An Enhanced and Validated Lexicon of Medical Concepts","translated_title":"","metadata":{"abstract":"Information extraction in the medical domain is laborious and time-consuming due to the insufficient number of domain-specific lexicons and lack of involvement of domain experts such as doctors and medical practitioners. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="91474035"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/91474035/An_Educational_Chatbot_for_Answering_Queries"><img alt="Research paper thumbnail of An Educational Chatbot for Answering Queries" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/91474035/An_Educational_Chatbot_for_Answering_Queries">An Educational Chatbot for Answering Queries</a></div><div class="wp-workCard_item"><span>Advances in Intelligent Systems and Computing</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The fast progress in the development of communication and information has made people very divers...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The fast progress in the development of communication and information has made people very diverse in knowledge improvement, education, and learning methods. In this paper, we show how we can convert documents into the knowledge of a chatter robot. It helps users to make profit out of it by asking and answering queries, using electronic documents which are integrated with the simulated system. Hence, we are motivated to develop an educational chatbot system which provides a virtual assistant. Its main function is to streamline and to automate manual and administrative tasks while supporting other course-related activities. The aim of this research work is to develop a system which is automated and can provide an answer to a question asked by a user on behalf of a person, for educational purposes. We have focused on the local as well as Web databases to make the model user-friendly, interactive, and scalable.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="91474035"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="91474035"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 91474035; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=91474035]").text(description); $(".js-view-count[data-work-id=91474035]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 91474035; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='91474035']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 91474035, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=91474035]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":91474035,"title":"An Educational Chatbot for Answering Queries","translated_title":"","metadata":{"abstract":"The fast progress in the development of communication and information has made people very diverse in knowledge improvement, education, and learning methods. 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We have focused on the local as well as Web databases to make the model user-friendly, interactive, and scalable.","publisher":"Springer Singapore","publication_date":{"day":null,"month":null,"year":2019,"errors":{}},"publication_name":"Advances in Intelligent Systems and Computing"},"translated_abstract":"The fast progress in the development of communication and information has made people very diverse in knowledge improvement, education, and learning methods. In this paper, we show how we can convert documents into the knowledge of a chatter robot. It helps users to make profit out of it by asking and answering queries, using electronic documents which are integrated with the simulated system. Hence, we are motivated to develop an educational chatbot system which provides a virtual assistant. Its main function is to streamline and to automate manual and administrative tasks while supporting other course-related activities. The aim of this research work is to develop a system which is automated and can provide an answer to a question asked by a user on behalf of a person, for educational purposes. We have focused on the local as well as Web databases to make the model user-friendly, interactive, and scalable.","internal_url":"https://www.academia.edu/91474035/An_Educational_Chatbot_for_Answering_Queries","translated_internal_url":"","created_at":"2022-11-23T16:34:53.680-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":11565886,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"An_Educational_Chatbot_for_Answering_Queries","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":11565886,"first_name":"Anupam","middle_initials":"","last_name":"Mondal","page_name":"AnupamMondal2","domain_name":"independent","created_at":"2014-04-27T21:03:49.131-07:00","display_name":"Anupam Mondal","url":"https://independent.academia.edu/AnupamMondal2"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":141114,"name":"World Wide Web","url":"https://www.academia.edu/Documents/in/World_Wide_Web"},{"id":184950,"name":"Question Answering","url":"https://www.academia.edu/Documents/in/Question_Answering"},{"id":246160,"name":"Chatbot","url":"https://www.academia.edu/Documents/in/Chatbot"},{"id":377043,"name":"Scalability","url":"https://www.academia.edu/Documents/in/Scalability"}],"urls":[{"id":26324617,"url":"http://link.springer.com/content/pdf/10.1007/978-981-13-7403-6_7"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="91474034"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/91474034/Chatbot_An_automated_conversation_system_for_the_educational_domain"><img alt="Research paper thumbnail of Chatbot: An automated conversation system for the educational domain" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/91474034/Chatbot_An_automated_conversation_system_for_the_educational_domain">Chatbot: An automated conversation system for the educational domain</a></div><div class="wp-workCard_item"><span>2018 International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP)</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Speech and textual information play a crucial role in communicating between humans. An article in...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Speech and textual information play a crucial role in communicating between humans. An article in “The New York Times” published that now-a-days the adults are spending more than 8 hours a day on screens of computers or mobiles. So the major communication between humans is conducted through web applications such as WhatsApp, Facebook, and Twitter etc as a form of speech and textual conversation. In the present paper, we have focused on designing a textual communication application namely chatbot in the educational domain. The proposed chatbot assists in answering questions provided by the users. To develop the system, we have employed an ensemble learning method as random forest in the presence of extracted features from our prepared dataset. Besides, the validation system offers an average F-measure 0.870 score on various K-values under random forest for the proposed chatbot. Finally, we have deployed the proposed system in a from of telegram bot.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="91474034"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="91474034"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 91474034; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=91474034]").text(description); $(".js-view-count[data-work-id=91474034]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 91474034; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='91474034']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 91474034, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=91474034]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":91474034,"title":"Chatbot: An automated conversation system for the educational domain","translated_title":"","metadata":{"abstract":"Speech and textual information play a crucial role in communicating between humans. An article in “The New York Times” published that now-a-days the adults are spending more than 8 hours a day on screens of computers or mobiles. So the major communication between humans is conducted through web applications such as WhatsApp, Facebook, and Twitter etc as a form of speech and textual conversation. In the present paper, we have focused on designing a textual communication application namely chatbot in the educational domain. The proposed chatbot assists in answering questions provided by the users. To develop the system, we have employed an ensemble learning method as random forest in the presence of extracted features from our prepared dataset. Besides, the validation system offers an average F-measure 0.870 score on various K-values under random forest for the proposed chatbot. Finally, we have deployed the proposed system in a from of telegram bot.","publisher":"IEEE","publication_date":{"day":null,"month":null,"year":2018,"errors":{}},"publication_name":"2018 International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP)"},"translated_abstract":"Speech and textual information play a crucial role in communicating between humans. An article in “The New York Times” published that now-a-days the adults are spending more than 8 hours a day on screens of computers or mobiles. So the major communication between humans is conducted through web applications such as WhatsApp, Facebook, and Twitter etc as a form of speech and textual conversation. In the present paper, we have focused on designing a textual communication application namely chatbot in the educational domain. The proposed chatbot assists in answering questions provided by the users. To develop the system, we have employed an ensemble learning method as random forest in the presence of extracted features from our prepared dataset. Besides, the validation system offers an average F-measure 0.870 score on various K-values under random forest for the proposed chatbot. Finally, we have deployed the proposed system in a from of telegram bot.","internal_url":"https://www.academia.edu/91474034/Chatbot_An_automated_conversation_system_for_the_educational_domain","translated_internal_url":"","created_at":"2022-11-23T16:34:53.518-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":11565886,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Chatbot_An_automated_conversation_system_for_the_educational_domain","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":11565886,"first_name":"Anupam","middle_initials":"","last_name":"Mondal","page_name":"AnupamMondal2","domain_name":"independent","created_at":"2014-04-27T21:03:49.131-07:00","display_name":"Anupam Mondal","url":"https://independent.academia.edu/AnupamMondal2"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":24342,"name":"Conversation","url":"https://www.academia.edu/Documents/in/Conversation"},{"id":246160,"name":"Chatbot","url":"https://www.academia.edu/Documents/in/Chatbot"}],"urls":[{"id":26324616,"url":"http://xplorestaging.ieee.org/ielx7/8681778/8692798/08692927.pdf?arnumber=8692927"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="91474033"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/91474033/Relation_Extraction_of_Medical_Concepts_Using_Categorization_and_Sentiment_Analysis"><img alt="Research paper thumbnail of Relation Extraction of Medical Concepts Using Categorization and Sentiment Analysis" class="work-thumbnail" src="https://attachments.academia-assets.com/94751726/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/91474033/Relation_Extraction_of_Medical_Concepts_Using_Categorization_and_Sentiment_Analysis">Relation Extraction of Medical Concepts Using Categorization and Sentiment Analysis</a></div><div class="wp-workCard_item"><span>Cognitive Computation</span><span>, 2018</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="df8083a5dcc05ccde48a5083ced4184f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":94751726,"asset_id":91474033,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/94751726/download_file?st=MTczMzAzNTQ1MCw4LjIyMi4yMDguMTQ2&st=MTczMzAzNTQ0OSw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="91474033"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="91474033"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 91474033; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=91474033]").text(description); $(".js-view-count[data-work-id=91474033]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 91474033; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='91474033']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 91474033, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "df8083a5dcc05ccde48a5083ced4184f" } } $('.js-work-strip[data-work-id=91474033]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":91474033,"title":"Relation Extraction of Medical Concepts Using Categorization and Sentiment Analysis","translated_title":"","metadata":{"publisher":"Springer Science and Business Media LLC","grobid_abstract":"In healthcare services, information extraction is the key to understand any corpus-based knowledge. The process becomes laborious when the annotation is done manually for the availability of a large number of text corpora. Hence, future automated extraction systems will be essential for groups of experts such as doctors and medical practitioners as well as nonexperts such as patients, to ensure enhanced clinical decision-making for improving healthcare systems. Such extraction systems can be developed using medical concepts and concept-related features as the part of a structured corpus. The latter can assist in assigning the category and sentiment to each of the medical concepts and their lexical contexts. These categories and sentiment assignments constitute semantic relations of medical concepts, with their context, represented by sentences of the corpus. This paper presents a new domain-based knowledge lexicon coupled with a machine learning approach to extract semantic relations. This is done by assigning category and sentiment of the medical concepts and contexts. The categories considered in this research, are diseases, symptoms, drugs, human anatomy, and miscellaneous medical terms, whereas sentiments are considered as positive and negative. The proposed assignment systems are developed on the top of WordNet of Medical Event (WME) lexicon. The developed lexicon provides medical concepts and their features, namely Parts-Of-Speech (POS), gloss (descriptive explanation), Similar Sentiment Words (SSW), affinity score, gravity score, polarity score, and sentiment. Several well-known supervised classifiers, including Naïve Bayes, Logistic Regression, and support vector-based Sequential Minimal Optimization (SMO) have been applied to evaluate the developed systems. The proposed approaches have resulted in a concepts clustering application by identifying the semantic relations of concepts. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="91474015"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/91474015/Relationship_Extraction_based_on_Category_of_Medical_Concepts_from_Lexical_Contexts"><img alt="Research paper thumbnail of Relationship Extraction based on Category of Medical Concepts from Lexical Contexts" class="work-thumbnail" src="https://attachments.academia-assets.com/94751696/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/91474015/Relationship_Extraction_based_on_Category_of_Medical_Concepts_from_Lexical_Contexts">Relationship Extraction based on Category of Medical Concepts from Lexical Contexts</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Medical information extraction is an emerging task in healthcare services aim to acquire crucial ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Medical information extraction is an emerging task in healthcare services aim to acquire crucial information of the concepts like diseases, symptoms, and drugs and also to identify their relations from corpora. In the present article, we have proposed a relationship extraction system based on such categories of medical concepts. We have employed rulebased as well as Support Vector Machine (SVM) based feature-oriented approach along with a domain-specific lexicon viz WordNet of Medical Event (WME 2.0). The lexicon assists in recognizing medical concepts and their related features like Parts-Of-Speech (POS), categories, and Similar Sentiment Words (SSW). We have opted only four fundamental categories diseases, drugs, symptoms, and human anatomy of medical concepts as provided in WME lexicon. Such categories play a crucial role in identifying eight types of different semantic relations viz. drug-drug, disease-drug, and human anatomy-symptom from the medical context. Thereafter, we have...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="be083a45d44d14c2b29c086713cd9e71" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":94751696,"asset_id":91474015,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/94751696/download_file?st=MTczMzAzNTQ1MCw4LjIyMi4yMDguMTQ2&st=MTczMzAzNTQ1MCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="91474015"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="91474015"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 91474015; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=91474015]").text(description); $(".js-view-count[data-work-id=91474015]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 91474015; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='91474015']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 91474015, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "be083a45d44d14c2b29c086713cd9e71" } } $('.js-work-strip[data-work-id=91474015]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":91474015,"title":"Relationship Extraction based on Category of Medical Concepts from Lexical Contexts","translated_title":"","metadata":{"abstract":"Medical information extraction is an emerging task in healthcare services aim to acquire crucial information of the concepts like diseases, symptoms, and drugs and also to identify their relations from corpora. In the present article, we have proposed a relationship extraction system based on such categories of medical concepts. We have employed rulebased as well as Support Vector Machine (SVM) based feature-oriented approach along with a domain-specific lexicon viz WordNet of Medical Event (WME 2.0). The lexicon assists in recognizing medical concepts and their related features like Parts-Of-Speech (POS), categories, and Similar Sentiment Words (SSW). We have opted only four fundamental categories diseases, drugs, symptoms, and human anatomy of medical concepts as provided in WME lexicon. Such categories play a crucial role in identifying eight types of different semantic relations viz. drug-drug, disease-drug, and human anatomy-symptom from the medical context. Thereafter, we have...","publisher":"ICON","publication_date":{"day":null,"month":null,"year":2017,"errors":{}}},"translated_abstract":"Medical information extraction is an emerging task in healthcare services aim to acquire crucial information of the concepts like diseases, symptoms, and drugs and also to identify their relations from corpora. In the present article, we have proposed a relationship extraction system based on such categories of medical concepts. We have employed rulebased as well as Support Vector Machine (SVM) based feature-oriented approach along with a domain-specific lexicon viz WordNet of Medical Event (WME 2.0). The lexicon assists in recognizing medical concepts and their related features like Parts-Of-Speech (POS), categories, and Similar Sentiment Words (SSW). We have opted only four fundamental categories diseases, drugs, symptoms, and human anatomy of medical concepts as provided in WME lexicon. Such categories play a crucial role in identifying eight types of different semantic relations viz. drug-drug, disease-drug, and human anatomy-symptom from the medical context. Thereafter, we have...","internal_url":"https://www.academia.edu/91474015/Relationship_Extraction_based_on_Category_of_Medical_Concepts_from_Lexical_Contexts","translated_internal_url":"","created_at":"2022-11-23T16:34:09.614-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":11565886,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":94751696,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/94751696/thumbnails/1.jpg","file_name":"W17-7527.pdf","download_url":"https://www.academia.edu/attachments/94751696/download_file?st=MTczMzAzNTQ1MCw4LjIyMi4yMDguMTQ2&st=MTczMzAzNTQ1MCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Relationship_Extraction_based_on_Categor.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/94751696/W17-7527-libre.pdf?1669252366=\u0026response-content-disposition=attachment%3B+filename%3DRelationship_Extraction_based_on_Categor.pdf\u0026Expires=1733039049\u0026Signature=SdVw1WKH8tku7mTyPm4eX-8ngTKdunEvojM7W2K-2T88m46Ft9OEbamgTAF6acDEZe3oGGyJC5KglfUrGQVd7eeW-iFT1GJnLR4Y0rj0-5Ebl62bCkHhY-f4qtkxxMf095GWvIpd9F9dUO80BUfwtvOaNLzWacUqlqgD4d0rEHBmYQCRgANGWhVSVA25pwoKb0FjMK0EvfG3c-7eYT~lBEsWh3Zul~kdV2zfZZfvX5~QZpnIyil0pX92YlQNBMBhuWzf9zbGappw5EzMAGNEbIt1csfb-7pvjax9YkE63gCw4DX8RjbgyaH8w-D5qNP48NjMWdL0KA2Ipd1m91LIMw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Relationship_Extraction_based_on_Category_of_Medical_Concepts_from_Lexical_Contexts","translated_slug":"","page_count":8,"language":"en","content_type":"Work","owner":{"id":11565886,"first_name":"Anupam","middle_initials":"","last_name":"Mondal","page_name":"AnupamMondal2","domain_name":"independent","created_at":"2014-04-27T21:03:49.131-07:00","display_name":"Anupam Mondal","url":"https://independent.academia.edu/AnupamMondal2"},"attachments":[{"id":94751696,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/94751696/thumbnails/1.jpg","file_name":"W17-7527.pdf","download_url":"https://www.academia.edu/attachments/94751696/download_file?st=MTczMzAzNTQ1MCw4LjIyMi4yMDguMTQ2&st=MTczMzAzNTQ1MCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Relationship_Extraction_based_on_Categor.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/94751696/W17-7527-libre.pdf?1669252366=\u0026response-content-disposition=attachment%3B+filename%3DRelationship_Extraction_based_on_Categor.pdf\u0026Expires=1733039050\u0026Signature=J9X4WYVvCA8dhZeTDjWSkbKnmjxKbf7VlODhGGJojzcYV1Nh-DZCK1AmKZOIoPQVLGWeA5nG5qdUH~~w1uaeNV2XC7wecB0Iv3OXRsFe4AKMNv99FHBtccvlbHeRTreDudFirPj0XTTcOZx3jBb-9nQChX8d0ZFzXKOVpJObQMWK9bumpUVxEPlmlLjEDwhgDU0NFmpPW0298wy~vKSyASH2HxiwzVPXqU920IIutxtneaHqjepmXX1U729zYAQNWk-ygN1TO5kIylD9M18UrHeyMi-WEMlflkv95dry0QrZ8h-~QeBK4n0qK4IVNG~rycMUuLMZ7BtPMaDBuxWi0g__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":1432,"name":"Natural Language Processing","url":"https://www.academia.edu/Documents/in/Natural_Language_Processing"}],"urls":[{"id":26324602,"url":"https://www.aclweb.org/anthology/W17-7527.pdf"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="84506515"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/84506515/Ensemble_approach_for_identifying_medical_concepts_with_special_attention_to_lexical_scope"><img alt="Research paper thumbnail of Ensemble approach for identifying medical concepts with special attention to lexical scope" class="work-thumbnail" src="https://attachments.academia-assets.com/89505492/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/84506515/Ensemble_approach_for_identifying_medical_concepts_with_special_attention_to_lexical_scope">Ensemble approach for identifying medical concepts with special attention to lexical scope</a></div><div class="wp-workCard_item"><span>Sādhanā</span><span>, 2021</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="235b65987a55c1296840a2d44719cda8" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":89505492,"asset_id":84506515,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/89505492/download_file?st=MTczMzAzNTQ1MCw4LjIyMi4yMDguMTQ2&st=MTczMzAzNTQ1MCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="84506515"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="84506515"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 84506515; 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This extraction process is laborious and time-consuming due to unavailability of medical experts. Thus, in the present task, we were motivated to develop an automated extraction system for identifying medical and non-medical concepts. These concepts help to extract the key information from medical corpora. Not only medical concepts but also their non-medical counterparts are equally important for diagnosis purposes. Hence, we have employed three different approaches such as unsupervised, supervised, and their combined ensemble version to identify both medical and non-medical terms (words/phrases). The unsupervised module consists of two phases: parts-ofspeech (POS) tagging followed by searching in a domain-specific lexicon, namely WordNet of Medical Event (WME 3.0). On the other hand the supervised module is designed by two machine learning classifiers, namely Naïve Bayes and Conditional Random Field (CRF) along with various features like category, POS, sentiment, etc. Finally, we have combined the important outcomes of unsupervised and supervised modules and developed two versions of ensemble module (Ensemble-I and Ensemble-II). All the modules identify uni-gram, bi-gram, tri-gram, and more than tri-gram medical concepts and separate non-medical words or phrases in a context. In order to evaluate all modules of concept identification system, we have prepared an experimental dataset. It has been split into three parts, namely training, development, and test. We observed that ensemble module provides better output in contrast with individual modules and Ensemble-I outperforms Ensemble-II in identifying medical concepts consisting of all possible n-grams. The result analysis shows that the F-measures of 0.91 and 0.94 have been obtained for identifying medical concepts and non-medical words/phrases using both of the ensemble modules, respectively. The present research reports the initial steps to build an automated concept identification framework in health-care. This system assists in designing various domain-specific applications like annotation, categorization, recommendation system, etc.","publication_date":{"day":null,"month":null,"year":2021,"errors":{}},"publication_name":"Sādhanā","grobid_abstract_attachment_id":89505492},"translated_abstract":null,"internal_url":"https://www.academia.edu/84506515/Ensemble_approach_for_identifying_medical_concepts_with_special_attention_to_lexical_scope","translated_internal_url":"","created_at":"2022-08-11T18:07:33.861-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":11565886,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":89505492,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/89505492/thumbnails/1.jpg","file_name":"s12046-021-01593-5.pdf","download_url":"https://www.academia.edu/attachments/89505492/download_file?st=MTczMzAzNTQ1MCw4LjIyMi4yMDguMTQ2&st=MTczMzAzNTQ1MCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Ensemble_approach_for_identifying_medica.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/89505492/s12046-021-01593-5-libre.pdf?1660271352=\u0026response-content-disposition=attachment%3B+filename%3DEnsemble_approach_for_identifying_medica.pdf\u0026Expires=1733039050\u0026Signature=Ca2hExE~0kLflE7d4JYKS2WTRzzpX5F8X70RlpmzI6ohCCkH6fX30KEjy6eYoH0gs2COMBcwhLFF5h8IvPbb0IyJzXGKyywEOoxVplojy6T8Qx3J4GYIELUfKGF49~CvZSXGyK49wh3RciEzkNBkAKOmRDFkpka6ZTk~kUm4bjwvZtcm5bFNSSWjkN0fEPHXoOYcnflUPzK8WtZ4bdWcpm9A5hJPFKJty~QSR-uaPj8b10N4pGRUvOmeBZk4SmWLLHJuCCpGfWbRBqpZkx6XZz5fvpI7JGSeTddUMKOsHpJDeZ2rsc7qeDLLH3J3CIYvjrz-5bkRRTuOMcJInpfWww__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Ensemble_approach_for_identifying_medical_concepts_with_special_attention_to_lexical_scope","translated_slug":"","page_count":12,"language":"en","content_type":"Work","owner":{"id":11565886,"first_name":"Anupam","middle_initials":"","last_name":"Mondal","page_name":"AnupamMondal2","domain_name":"independent","created_at":"2014-04-27T21:03:49.131-07:00","display_name":"Anupam Mondal","url":"https://independent.academia.edu/AnupamMondal2"},"attachments":[{"id":89505492,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/89505492/thumbnails/1.jpg","file_name":"s12046-021-01593-5.pdf","download_url":"https://www.academia.edu/attachments/89505492/download_file?st=MTczMzAzNTQ1MCw4LjIyMi4yMDguMTQ2&st=MTczMzAzNTQ1MCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Ensemble_approach_for_identifying_medica.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/89505492/s12046-021-01593-5-libre.pdf?1660271352=\u0026response-content-disposition=attachment%3B+filename%3DEnsemble_approach_for_identifying_medica.pdf\u0026Expires=1733039050\u0026Signature=Ca2hExE~0kLflE7d4JYKS2WTRzzpX5F8X70RlpmzI6ohCCkH6fX30KEjy6eYoH0gs2COMBcwhLFF5h8IvPbb0IyJzXGKyywEOoxVplojy6T8Qx3J4GYIELUfKGF49~CvZSXGyK49wh3RciEzkNBkAKOmRDFkpka6ZTk~kUm4bjwvZtcm5bFNSSWjkN0fEPHXoOYcnflUPzK8WtZ4bdWcpm9A5hJPFKJty~QSR-uaPj8b10N4pGRUvOmeBZk4SmWLLHJuCCpGfWbRBqpZkx6XZz5fvpI7JGSeTddUMKOsHpJDeZ2rsc7qeDLLH3J3CIYvjrz-5bkRRTuOMcJInpfWww__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":48,"name":"Engineering","url":"https://www.academia.edu/Documents/in/Engineering"},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence"},{"id":1009202,"name":"Sadhana","url":"https://www.academia.edu/Documents/in/Sadhana"}],"urls":[{"id":22807570,"url":"https://link.springer.com/content/pdf/10.1007/s12046-021-01593-5.pdf"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="84506514"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/84506514/A_Hybrid_Approach_Based_Sentiment_Extraction_from_Medical_Context"><img alt="Research paper thumbnail of A Hybrid Approach Based Sentiment Extraction from Medical Context" class="work-thumbnail" src="https://attachments.academia-assets.com/89505490/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/84506514/A_Hybrid_Approach_Based_Sentiment_Extraction_from_Medical_Context">A Hybrid Approach Based Sentiment Extraction from Medical Context</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In the domain of Bio medical Natural Language Processing (Bio-NLP), the information extraction an...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In the domain of Bio medical Natural Language Processing (Bio-NLP), the information extraction and context sentiment identification are treated as emerging tasks. Several linguistic features like negation , uni-gram, bi-gram, Part-of-Speech (POS) have been used to extract the medical concepts and their sense-based context level information. Thus, in the present attempt, a hybrid approach which is the combination of both linguistic and machine learning approaches has been introduced to extract the contextual sense-based information from a medical corpus. The extraction of sentiment oriented keywords is the crucial part towards identifying the senses of medical contexts. In our previous work, we have developed a medical sense-based lexicon known as WordNet of Medical Event (WME). Several sentiment lexicons like Senti-WordNet, SenticNet etc. were used to represent WME. 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The relations bond ideas, arguments, thoughts, and feelings, re-shape them during a speech, and finally build knowledge out of all information provided in the conversation. Speakers share a common interest to discuss. It is expected that each speaker&#39;s reply includes duplicated forms of words from previous speakers. However, linguistic adaptation is observed and evolves in a more complex path than just transferring slightly modified versions of common concepts. A conversation aiming a benefit at the end shows an emergent cooperation inducing the adaptation. Not only cooperation, but also competition drives the adaptation or an opposite scenario and one can capture the dynamic process by tracking how the concepts are linguistically linked. To uncover salient complex dynamic events in verbal communications, we attempt to discover self-organized linguistic relations h...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="eccd0341d213687aa55b0f819185a82b" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":89505489,"asset_id":84506513,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/89505489/download_file?st=MTczMzAzNTQ1MCw4LjIyMi4yMDguMTQ2&st=MTczMzAzNTQ1MCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="84506513"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="84506513"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 84506513; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=84506513]").text(description); $(".js-view-count[data-work-id=84506513]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 84506513; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='84506513']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 84506513, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "eccd0341d213687aa55b0f819185a82b" } } $('.js-work-strip[data-work-id=84506513]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":84506513,"title":"Tracing Linguistic Relations in Winning and Losing Sides of Explicit Opposing Groups","translated_title":"","metadata":{"abstract":"Linguistic relations in oral conversations present how opinions are constructed and developed in a restricted time. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="84506510"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/84506510/A_Supervised_Framework_for_Classifying_Dependency_Relations_from_Bengali_Shallow_Parsed_Sentences"><img alt="Research paper thumbnail of A Supervised Framework for Classifying Dependency Relations from Bengali Shallow Parsed Sentences" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/84506510/A_Supervised_Framework_for_Classifying_Dependency_Relations_from_Bengali_Shallow_Parsed_Sentences">A Supervised Framework for Classifying Dependency Relations from Bengali Shallow Parsed Sentences</a></div><div class="wp-workCard_item"><span>Lecture Notes in Computer Science</span><span>, 2015</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Natural Language Processing, one of the contemporary research area has adopted parsing technologi...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Natural Language Processing, one of the contemporary research area has adopted parsing technologies for various languages across the world for different objectives. In the present task, a new approach has been introduced for classifying the dependency parsed relations for a morphologically rich and free-phrase-ordered Indian language like Bengali. The pair of dependency parsed relations also referred as kaarakas &#39;cases&#39; are classified based on different features like vibhaktis inflections, Part-of-Speech POS, punctuation, gender, number and post-position. It is observed that the consecutive and non-consecutive occurrences of such relations play a vital role in the classification. We employed three different machine-learning classifiers, namely NaiveBayes, Sequential Minimal Optimization SMO and Conditional Random Field CRF which obtained the average F-Scores of 0.895, 0.869 and 0.697, respectively for classifying relation pairs of three primary kaarakas and one primary vibhakti relation. 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We have also conducted the error analysis for such primary relations using confusion matrices.","publication_date":{"day":null,"month":null,"year":2015,"errors":{}},"publication_name":"Lecture Notes in Computer Science"},"translated_abstract":"Natural Language Processing, one of the contemporary research area has adopted parsing technologies for various languages across the world for different objectives. In the present task, a new approach has been introduced for classifying the dependency parsed relations for a morphologically rich and free-phrase-ordered Indian language like Bengali. The pair of dependency parsed relations also referred as kaarakas \u0026#39;cases\u0026#39; are classified based on different features like vibhaktis inflections, Part-of-Speech POS, punctuation, gender, number and post-position. It is observed that the consecutive and non-consecutive occurrences of such relations play a vital role in the classification. 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Category describes how medical concepts are fundamentally separated from each other to represent their conceptual knowledge in the corpus. In this paper, we focus on identifying the category of medical concepts and contexts which describe the subjective and the conceptual information of the medical corpus. To recognize the medical concept and assign their category, we employ our previously developed WordNet of Medical Event (WME 2.0) domain-specific lexicon. The lexicon provides medical concepts and their affinity, gravity, polarity scores, similar sentiment words, and sentiment features, help to develop the category assignment system. The identified categories for the concepts are diseases, drugs, symptoms, human_anatomy, and miscellaneous medical terms (MMT), which all refer the broadest fundamental classes of medical concepts. Therefore, the assigned categories of medical concepts used to build the category assignment system for the medical context. The proposed system allows extracting eleven types of pairbased categories as disease-symptom, disease-drug, and disease-MMT of contexts. To validate the categorization system for medical concepts and contexts, we have employed widely used supervised machine learning classifiers namely Naïve Bayes and Logistic Regression in the presence of WME 2.0 lexicon. 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The proposed system allows extracting eleven types of pairbased categories as disease-symptom, disease-drug, and disease-MMT of contexts. To validate the categorization system for medical concepts and contexts, we have employed widely used supervised machine learning classifiers namely Naïve Bayes and Logistic Regression in the presence of WME 2.0 lexicon. The two classifiers provide F-scores of 0.81 and 0.86 for the concepts and contexts categorization systems, respectively.","publisher":"IEEE","publication_date":{"day":null,"month":null,"year":2017,"errors":{}},"publication_name":"2017 IEEE Symposium Series on Computational Intelligence (SSCI)"},"translated_abstract":"In healthcare, information extraction is important in order to identify conceptual knowledge as a category of medical concepts from a large number of unstructured and semi-structured corpora. 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The proposed system allows extracting eleven types of pairbased categories as disease-symptom, disease-drug, and disease-MMT of contexts. To validate the categorization system for medical concepts and contexts, we have employed widely used supervised machine learning classifiers namely Naïve Bayes and Logistic Regression in the presence of WME 2.0 lexicon. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="29412504"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/29412504/Lexical_Resource_for_Medical_Events_A_Polarity_Based_Approach"><img alt="Research paper thumbnail of Lexical Resource for Medical Events: A Polarity Based Approach" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/29412504/Lexical_Resource_for_Medical_Events_A_Polarity_Based_Approach">Lexical Resource for Medical Events: A Polarity Based Approach</a></div><div class="wp-workCard_item"><span>2015 IEEE International Conference on Data Mining Workshop (ICDMW)</span><span>, 2015</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="29412504"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="29412504"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 29412504; 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