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true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="118332116"><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/118332116/Understanding_Cohesion_in_Writings_and_Speech_of_Schizophrenia_Patients"><img alt="Research paper thumbnail of Understanding Cohesion in Writings and Speech of Schizophrenia Patients" 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/118332116/Understanding_Cohesion_in_Writings_and_Speech_of_Schizophrenia_Patients">Understanding Cohesion in Writings and Speech of Schizophrenia Patients</a></div><div class="wp-workCard_item"><span>2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Schizophrenia is one of the mental disorders that impacts a person&#39;s thinking, speech, and ac...</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">Schizophrenia is one of the mental disorders that impacts a person&#39;s thinking, speech, and actions. It can reduce a person’s ability to process auditory information and make decisions. Analyzing this disorder correctly is important because it might help with different ways of reducing its negative effects on its patients. Linguists and psychiatrists have been investigating language impairments and speech disorder in people with schizophrenia disorder which can be challenging. In this study, we attempt to address this issue by analyzing linguistic features i.e. cohesion in the writings and speech scripts of schizophrenia patients. Our results show that using referential cohesion with text easability or situation model features provides the best performance for speech whereas for writing dataset, readability or a combination of situation model and readability yield the best performance.</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="118332116"><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="118332116"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 118332116; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=118332116]").text(description); $(".js-view-count[data-work-id=118332116]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 118332116; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='118332116']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 118332116, 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" } } $('.js-work-strip[data-work-id=118332116]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":118332116,"title":"Understanding Cohesion in Writings and Speech of Schizophrenia Patients","translated_title":"","metadata":{"abstract":"Schizophrenia is one of the mental disorders that impacts a person\u0026#39;s thinking, speech, and actions. 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We propose a transfer method that employs information from source and target syntactic dependencies as well as word alignment density to improve the quality of an iterative bootstrapping method. 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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="118332110"><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/118332110/Annotated_corpora_and_tools_of_the_PARSEME_Shared_Task_on_Automatic_Identification_of_Verbal_Multiword_Expressions_edition_1_1_"><img alt="Research paper thumbnail of Annotated corpora and tools of the PARSEME Shared Task on Automatic Identification of Verbal Multiword Expressions (edition 1.1)" 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/118332110/Annotated_corpora_and_tools_of_the_PARSEME_Shared_Task_on_Automatic_Identification_of_Verbal_Multiword_Expressions_edition_1_1_">Annotated corpora and tools of the PARSEME Shared Task on Automatic Identification of Verbal Multiword Expressions (edition 1.1)</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This multilingual resource contains corpora in which verbal MWEs have been manually annotated. VM...</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 multilingual resource contains corpora in which verbal MWEs have been manually annotated. VMWEs include idioms (let the cat out of the bag), light-verb constructions (make a decision), verb-particle constructions (give up), inherently reflexive verbs (help oneself), and multi-verb constructions (make do). VMWEs were annotated according to the universal guidelines in 19 languages. The corpora are provided in the cupt format, inspired by the CONLL-U format. The corpora were used in the 1.1 edition of the PARSEME Shared Task (2018). For most languages, morphological and syntactic information – not necessarily using UD tagsets – including parts of speech, lemmas, morphological features and/or syntactic dependencies are also provided. Depending on the language, the information comes from treebanks (e.g., Universal Dependencies) or from automatic parsers trained on treebanks (e.g., UDPipe). This item contains training, development and test data, as well as the evaluation tools us...</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="118332110"><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="118332110"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 118332110; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=118332110]").text(description); $(".js-view-count[data-work-id=118332110]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 118332110; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='118332110']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 118332110, 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" } } $('.js-work-strip[data-work-id=118332110]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":118332110,"title":"Annotated corpora and tools of the PARSEME Shared Task on Automatic Identification of Verbal Multiword Expressions (edition 1.1)","translated_title":"","metadata":{"abstract":"This multilingual resource contains corpora in which verbal MWEs have been manually annotated. VMWEs include idioms (let the cat out of the bag), light-verb constructions (make a decision), verb-particle constructions (give up), inherently reflexive verbs (help oneself), and multi-verb constructions (make do). VMWEs were annotated according to the universal guidelines in 19 languages. The corpora are provided in the cupt format, inspired by the CONLL-U format. The corpora were used in the 1.1 edition of the PARSEME Shared Task (2018). For most languages, morphological and syntactic information – not necessarily using UD tagsets – including parts of speech, lemmas, morphological features and/or syntactic dependencies are also provided. Depending on the language, the information comes from treebanks (e.g., Universal Dependencies) or from automatic parsers trained on treebanks (e.g., UDPipe). 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Our method depends on having a WSD system for English and an automatic word alignment method. Crucially the approach relies on having parallel corpora. For Task 2 (Sinha et al., 2009) we apply a supervised WSD system to derive the English word senses. For Task 3 (Lefever & Hoste, 2009), we apply an unsupervised approach to the training and test data. Both of our systems that participated in Task 2 achieve a decent ranking among the participating systems. For Task 3 we achieve the highest ranking on several of the language pairs: French, German and Italian.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="5357dfaf20545153da6885319d14c913" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":113984325,"asset_id":118332109,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/113984325/download_file?st=MTczMjQ5NDcwNSw4LjIyMi4yMDguMTQ2&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="118332109"><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="118332109"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 118332109; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=118332109]").text(description); $(".js-view-count[data-work-id=118332109]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 118332109; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='118332109']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 118332109, 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: "5357dfaf20545153da6885319d14c913" } } $('.js-work-strip[data-work-id=118332109]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":118332109,"title":"COLEPL and COLSLM: An Unsupervised WSD Approach to Multilingual Lexical Substitution, Tasks 2 and 3 SemEval 2010","translated_title":"","metadata":{"abstract":"In this paper, we present a word sense disambiguation (WSD) based system for multilingual lexical substitution. Our method depends on having a WSD system for English and an automatic word alignment method. Crucially the approach relies on having parallel corpora. For Task 2 (Sinha et al., 2009) we apply a supervised WSD system to derive the English word senses. For Task 3 (Lefever \u0026 Hoste, 2009), we apply an unsupervised approach to the training and test data. Both of our systems that participated in Task 2 achieve a decent ranking among the participating systems. For Task 3 we achieve the highest ranking on several of the language pairs: French, German and Italian.","publisher":"*SEMEVAL","publication_date":{"day":null,"month":null,"year":2010,"errors":{}}},"translated_abstract":"In this paper, we present a word sense disambiguation (WSD) based system for multilingual lexical substitution. Our method depends on having a WSD system for English and an automatic word alignment method. Crucially the approach relies on having parallel corpora. 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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="118332107"><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/118332107/On_Arabic_Multi_Genre_Corpus_Diacritization"><img alt="Research paper thumbnail of On Arabic Multi-Genre Corpus Diacritization" 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/118332107/On_Arabic_Multi_Genre_Corpus_Diacritization">On Arabic Multi-Genre Corpus Diacritization</a></div><div class="wp-workCard_item"><span>Qatar Foundation Annual Research Conference Proceedings Volume 2016 Issue 1</span><span>, 2016</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">One of the characteristics of writing in Modern Standard Arabic (MSA) is that the commonly used o...</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">One of the characteristics of writing in Modern Standard Arabic (MSA) is that the commonly used orthography is mostly consonantal and does not provide full vocalization of the text. It sometimes includes optional diacritical marks (henceforth, diacritics or vowels).Arabic script consists of two classes of symbols: letters and diacritics. Letters comprise long vowels such as A, y, w as well as consonants. Diacritics on the other hand comprise short vowels, gemination markers, nunation markers, as well as other markers (such as hamza, the glottal stop which appears in conjunction with a small number of letters, dots on letters, elongation and emphatic markers) which in all, if present, render a more or less exact precise reading of a word. In this study, we are mostly addressing three types of diacritical marks: short vowels, nunation, and shadda (gemination).Diacritics are extremely useful for text readability and understanding. 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Their absence in Arabic text adds another layer of lexi...","publisher":"Hamad bin Khalifa University Press (HBKU Press)","publication_date":{"day":null,"month":null,"year":2016,"errors":{}},"publication_name":"Qatar Foundation Annual Research Conference Proceedings Volume 2016 Issue 1"},"translated_abstract":"One of the characteristics of writing in Modern Standard Arabic (MSA) is that the commonly used orthography is mostly consonantal and does not provide full vocalization of the text. It sometimes includes optional diacritical marks (henceforth, diacritics or vowels).Arabic script consists of two classes of symbols: letters and diacritics. Letters comprise long vowels such as A, y, w as well as consonants. Diacritics on the other hand comprise short vowels, gemination markers, nunation markers, as well as other markers (such as hamza, the glottal stop which appears in conjunction with a small number of letters, dots on letters, elongation and emphatic markers) which in all, if present, render a more or less exact precise reading of a word. In this study, we are mostly addressing three types of diacritical marks: short vowels, nunation, and shadda (gemination).Diacritics are extremely useful for text readability and understanding. Their absence in Arabic text adds another layer of lexi...","internal_url":"https://www.academia.edu/118332107/On_Arabic_Multi_Genre_Corpus_Diacritization","translated_internal_url":"","created_at":"2024-04-30T07:31:02.938-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32411980,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"On_Arabic_Multi_Genre_Corpus_Diacritization","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":32411980,"first_name":"Mona","middle_initials":null,"last_name":"Diab","page_name":"MDiab","domain_name":"gwu","created_at":"2015-06-21T18:43:17.283-07:00","display_name":"Mona Diab","url":"https://gwu.academia.edu/MDiab"},"attachments":[],"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"},{"id":3324,"name":"Arabic","url":"https://www.academia.edu/Documents/in/Arabic"},{"id":15674,"name":"Linguistics","url":"https://www.academia.edu/Documents/in/Linguistics"}],"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="118332106"><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/118332106/Building_a_Rich_Lexical_Resource_for_Standard_Arabic"><img alt="Research paper thumbnail of Building a Rich Lexical Resource for Standard Arabic" 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/118332106/Building_a_Rich_Lexical_Resource_for_Standard_Arabic">Building a Rich Lexical Resource for Standard Arabic</a></div><div class="wp-workCard_item"><span>Qatar Foundation Annual Research Conference Proceedings Volume 2018 Issue 4</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Language ambiguity is an inherent characteristic of natural languages. It refers to the phenomeno...</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">Language ambiguity is an inherent characteristic of natural languages. It refers to the phenomenon where an instance can be interpreted in multiple ways. Ambiguity is at the core of the problems faced by natural language processing applications (Obeid et al. 2013). Although humans have the ability to resolve such ambiguity based on their prior knowledge and context, there are instances (sentences, words,... etc) that require multiple readings to resolve it within a context (Hawwari et al. 2013; Diab et al. 2008). The problem of natural language ambiguity is further exacerbated by conventional orthographic decisions where not all phonemes are explicitly represented (Maamouri et al. 2010; Maamouri et al. 2012). Arabic standard orthography is one of these languages that is underspecified for some of the characters such as short vowels, gemination, glottal stops, etc which are collectively represented as diacritics (Zaghouani et al. 2012; Zaghouani et al. 2016). Most typical text in Ara...</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="118332106"><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="118332106"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 118332106; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=118332106]").text(description); $(".js-view-count[data-work-id=118332106]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 118332106; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='118332106']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 118332106, 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" } } $('.js-work-strip[data-work-id=118332106]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":118332106,"title":"Building a Rich Lexical Resource for Standard Arabic","translated_title":"","metadata":{"abstract":"Language ambiguity is an inherent characteristic of natural languages. It refers to the phenomenon where an instance can be interpreted in multiple ways. Ambiguity is at the core of the problems faced by natural language processing applications (Obeid et al. 2013). Although humans have the ability to resolve such ambiguity based on their prior knowledge and context, there are instances (sentences, words,... etc) that require multiple readings to resolve it within a context (Hawwari et al. 2013; Diab et al. 2008). The problem of natural language ambiguity is further exacerbated by conventional orthographic decisions where not all phonemes are explicitly represented (Maamouri et al. 2010; Maamouri et al. 2012). Arabic standard orthography is one of these languages that is underspecified for some of the characters such as short vowels, gemination, glottal stops, etc which are collectively represented as diacritics (Zaghouani et al. 2012; Zaghouani et al. 2016). Most typical text in Ara...","publisher":"Hamad bin Khalifa University Press (HBKU Press)","publication_date":{"day":null,"month":null,"year":2018,"errors":{}},"publication_name":"Qatar Foundation Annual Research Conference Proceedings Volume 2018 Issue 4"},"translated_abstract":"Language ambiguity is an inherent characteristic of natural languages. It refers to the phenomenon where an instance can be interpreted in multiple ways. Ambiguity is at the core of the problems faced by natural language processing applications (Obeid et al. 2013). Although humans have the ability to resolve such ambiguity based on their prior knowledge and context, there are instances (sentences, words,... etc) that require multiple readings to resolve it within a context (Hawwari et al. 2013; Diab et al. 2008). The problem of natural language ambiguity is further exacerbated by conventional orthographic decisions where not all phonemes are explicitly represented (Maamouri et al. 2010; Maamouri et al. 2012). Arabic standard orthography is one of these languages that is underspecified for some of the characters such as short vowels, gemination, glottal stops, etc which are collectively represented as diacritics (Zaghouani et al. 2012; Zaghouani et al. 2016). Most typical text in Ara...","internal_url":"https://www.academia.edu/118332106/Building_a_Rich_Lexical_Resource_for_Standard_Arabic","translated_internal_url":"","created_at":"2024-04-30T07:31:02.714-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32411980,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Building_a_Rich_Lexical_Resource_for_Standard_Arabic","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":32411980,"first_name":"Mona","middle_initials":null,"last_name":"Diab","page_name":"MDiab","domain_name":"gwu","created_at":"2015-06-21T18:43:17.283-07:00","display_name":"Mona Diab","url":"https://gwu.academia.edu/MDiab"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":3324,"name":"Arabic","url":"https://www.academia.edu/Documents/in/Arabic"}],"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="118332105"><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/118332105/Unsupervised_Word_Mapping_Using_Structural_Similarities_in_Monolingual_Embeddings"><img alt="Research paper thumbnail of Unsupervised Word Mapping Using Structural Similarities in Monolingual Embeddings" class="work-thumbnail" src="https://attachments.academia-assets.com/113984324/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/118332105/Unsupervised_Word_Mapping_Using_Structural_Similarities_in_Monolingual_Embeddings">Unsupervised Word Mapping Using Structural Similarities in Monolingual Embeddings</a></div><div class="wp-workCard_item"><span>Transactions of the Association for Computational Linguistics</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Most existing methods for automatic bilingual dictionary induction rely on prior alignments betwe...</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">Most existing methods for automatic bilingual dictionary induction rely on prior alignments between the source and target languages, such as parallel corpora or seed dictionaries. For many language pairs, such supervised alignments are not readily available. We propose an unsupervised approach for learning a bilingual dictionary for a pair of languages given their independently-learned monolingual word embeddings. The proposed method exploits local and global structures in monolingual vector spaces to align them such that similar words are mapped to each other. We show empirically that the performance of bilingual correspondents that are learned using our proposed unsupervised method is comparable to that of using supervised bilingual correspondents from a seed dictionary.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="ad11e7a413311d938d3a6e332ff0ba02" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":113984324,"asset_id":118332105,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/113984324/download_file?st=MTczMjQ5NDcwNSw4LjIyMi4yMDguMTQ2&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="118332105"><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="118332105"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 118332105; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=118332105]").text(description); $(".js-view-count[data-work-id=118332105]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 118332105; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='118332105']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 118332105, 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: "ad11e7a413311d938d3a6e332ff0ba02" } } $('.js-work-strip[data-work-id=118332105]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":118332105,"title":"Unsupervised Word Mapping Using Structural Similarities in Monolingual Embeddings","translated_title":"","metadata":{"abstract":"Most existing methods for automatic bilingual dictionary induction rely on prior alignments between the source and target languages, such as parallel corpora or seed dictionaries. 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For many language pairs, such supervised alignments are not readily available. We propose an unsupervised approach for learning a bilingual dictionary for a pair of languages given their independently-learned monolingual word embeddings. The proposed method exploits local and global structures in monolingual vector spaces to align them such that similar words are mapped to each other. 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We present several models of semantic similarity, based on diering representational assumptions, and investigate their properties via comparison with human ratings of verb similarity. The results oer insight into the bases for human similarity judgments and provide a testbed for further investigation of the interactions among syn- tactic properties, semantic structure, and semantic con- tent. Introduction The way we model semantic similarity is closely tied to our understanding of how linguistic representations are acquired and used. Some models of similarity, such as Tversky&#39;s (1977), assume an explicit set of features over which a similarity measure can be computed, a...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="5cb511f45119da671ee1d58c24e4cb5e" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":113984307,"asset_id":118332009,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/113984307/download_file?st=MTczMjQ5NDcwNiw4LjIyMi4yMDguMTQ2&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="118332009"><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="118332009"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 118332009; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=118332009]").text(description); $(".js-view-count[data-work-id=118332009]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 118332009; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='118332009']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 118332009, 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: "5cb511f45119da671ee1d58c24e4cb5e" } } $('.js-work-strip[data-work-id=118332009]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":118332009,"title":"Measuring Verb Similarity - eScholarship","translated_title":"","metadata":{"abstract":"Measuring Verb Similarity Philip Resnik and Mona Diab Department of Linguistics and Institute for Advanced Computer Studies University of Maryland College Park, MD USA f resnik,mdiab g @umiacs.umd.edu Abstract The way we model semantic similarity is closely tied to our understanding of linguistic representations. 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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="118332116"><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/118332116/Understanding_Cohesion_in_Writings_and_Speech_of_Schizophrenia_Patients"><img alt="Research paper thumbnail of Understanding Cohesion in Writings and Speech of Schizophrenia Patients" 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/118332116/Understanding_Cohesion_in_Writings_and_Speech_of_Schizophrenia_Patients">Understanding Cohesion in Writings and Speech of Schizophrenia Patients</a></div><div class="wp-workCard_item"><span>2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Schizophrenia is one of the mental disorders that impacts a person&#39;s thinking, speech, and ac...</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">Schizophrenia is one of the mental disorders that impacts a person&#39;s thinking, speech, and actions. It can reduce a person’s ability to process auditory information and make decisions. Analyzing this disorder correctly is important because it might help with different ways of reducing its negative effects on its patients. Linguists and psychiatrists have been investigating language impairments and speech disorder in people with schizophrenia disorder which can be challenging. In this study, we attempt to address this issue by analyzing linguistic features i.e. cohesion in the writings and speech scripts of schizophrenia patients. Our results show that using referential cohesion with text easability or situation model features provides the best performance for speech whereas for writing dataset, readability or a combination of situation model and readability yield the best performance.</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="118332116"><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="118332116"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 118332116; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=118332116]").text(description); $(".js-view-count[data-work-id=118332116]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 118332116; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='118332116']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 118332116, 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" } } $('.js-work-strip[data-work-id=118332116]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":118332116,"title":"Understanding Cohesion in Writings and Speech of Schizophrenia Patients","translated_title":"","metadata":{"abstract":"Schizophrenia is one of the mental disorders that impacts a person\u0026#39;s thinking, speech, and actions. 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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="118332111"><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/118332111/Transferring_Semantic_Roles_Using_Translation_and_Syntactic_Information"><img alt="Research paper thumbnail of Transferring Semantic Roles Using Translation and Syntactic Information" class="work-thumbnail" src="https://attachments.academia-assets.com/113984329/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/118332111/Transferring_Semantic_Roles_Using_Translation_and_Syntactic_Information">Transferring Semantic Roles Using Translation and Syntactic Information</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Our paper addresses the problem of annotation projection for semantic role labeling for resource-...</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">Our paper addresses the problem of annotation projection for semantic role labeling for resource-poor languages using supervised annotations from a resource-rich language through parallel data. We propose a transfer method that employs information from source and target syntactic dependencies as well as word alignment density to improve the quality of an iterative bootstrapping method. Our experiments yield a 3.5 absolute labeled F-score improvement over a standard annotation projection method.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="eba0042cadd3d5a47ea63bc76715c1d0" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":113984329,"asset_id":118332111,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/113984329/download_file?st=MTczMjQ5NDcwNiw4LjIyMi4yMDguMTQ2&st=MTczMjQ5NDcwNSw4LjIyMi4yMDguMTQ2&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="118332111"><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="118332111"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 118332111; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=118332111]").text(description); $(".js-view-count[data-work-id=118332111]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 118332111; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='118332111']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 118332111, 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: "eba0042cadd3d5a47ea63bc76715c1d0" } } $('.js-work-strip[data-work-id=118332111]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":118332111,"title":"Transferring Semantic Roles Using Translation and Syntactic Information","translated_title":"","metadata":{"abstract":"Our paper addresses the problem of annotation projection for semantic role labeling for resource-poor languages using supervised annotations from a resource-rich language through parallel 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="118332110"><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/118332110/Annotated_corpora_and_tools_of_the_PARSEME_Shared_Task_on_Automatic_Identification_of_Verbal_Multiword_Expressions_edition_1_1_"><img alt="Research paper thumbnail of Annotated corpora and tools of the PARSEME Shared Task on Automatic Identification of Verbal Multiword Expressions (edition 1.1)" 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/118332110/Annotated_corpora_and_tools_of_the_PARSEME_Shared_Task_on_Automatic_Identification_of_Verbal_Multiword_Expressions_edition_1_1_">Annotated corpora and tools of the PARSEME Shared Task on Automatic Identification of Verbal Multiword Expressions (edition 1.1)</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This multilingual resource contains corpora in which verbal MWEs have been manually annotated. VM...</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 multilingual resource contains corpora in which verbal MWEs have been manually annotated. VMWEs include idioms (let the cat out of the bag), light-verb constructions (make a decision), verb-particle constructions (give up), inherently reflexive verbs (help oneself), and multi-verb constructions (make do). VMWEs were annotated according to the universal guidelines in 19 languages. The corpora are provided in the cupt format, inspired by the CONLL-U format. The corpora were used in the 1.1 edition of the PARSEME Shared Task (2018). For most languages, morphological and syntactic information – not necessarily using UD tagsets – including parts of speech, lemmas, morphological features and/or syntactic dependencies are also provided. Depending on the language, the information comes from treebanks (e.g., Universal Dependencies) or from automatic parsers trained on treebanks (e.g., UDPipe). This item contains training, development and test data, as well as the evaluation tools us...</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="118332110"><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="118332110"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 118332110; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=118332110]").text(description); $(".js-view-count[data-work-id=118332110]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 118332110; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='118332110']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 118332110, 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" } } $('.js-work-strip[data-work-id=118332110]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":118332110,"title":"Annotated corpora and tools of the PARSEME Shared Task on Automatic Identification of Verbal Multiword Expressions (edition 1.1)","translated_title":"","metadata":{"abstract":"This multilingual resource contains corpora in which verbal MWEs have been manually annotated. VMWEs include idioms (let the cat out of the bag), light-verb constructions (make a decision), verb-particle constructions (give up), inherently reflexive verbs (help oneself), and multi-verb constructions (make do). VMWEs were annotated according to the universal guidelines in 19 languages. The corpora are provided in the cupt format, inspired by the CONLL-U format. The corpora were used in the 1.1 edition of the PARSEME Shared Task (2018). For most languages, morphological and syntactic information – not necessarily using UD tagsets – including parts of speech, lemmas, morphological features and/or syntactic dependencies are also provided. Depending on the language, the information comes from treebanks (e.g., Universal Dependencies) or from automatic parsers trained on treebanks (e.g., UDPipe). 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Our method depends on having a WSD system for English and an automatic word alignment method. Crucially the approach relies on having parallel corpora. For Task 2 (Sinha et al., 2009) we apply a supervised WSD system to derive the English word senses. For Task 3 (Lefever & Hoste, 2009), we apply an unsupervised approach to the training and test data. Both of our systems that participated in Task 2 achieve a decent ranking among the participating systems. For Task 3 we achieve the highest ranking on several of the language pairs: French, German and Italian.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="5357dfaf20545153da6885319d14c913" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":113984325,"asset_id":118332109,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/113984325/download_file?st=MTczMjQ5NDcwNiw4LjIyMi4yMDguMTQ2&st=MTczMjQ5NDcwNSw4LjIyMi4yMDguMTQ2&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="118332109"><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="118332109"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 118332109; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=118332109]").text(description); $(".js-view-count[data-work-id=118332109]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 118332109; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='118332109']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 118332109, 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: "5357dfaf20545153da6885319d14c913" } } $('.js-work-strip[data-work-id=118332109]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":118332109,"title":"COLEPL and COLSLM: An Unsupervised WSD Approach to Multilingual Lexical Substitution, Tasks 2 and 3 SemEval 2010","translated_title":"","metadata":{"abstract":"In this paper, we present a word sense disambiguation (WSD) based system for multilingual lexical substitution. 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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="118332107"><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/118332107/On_Arabic_Multi_Genre_Corpus_Diacritization"><img alt="Research paper thumbnail of On Arabic Multi-Genre Corpus Diacritization" 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/118332107/On_Arabic_Multi_Genre_Corpus_Diacritization">On Arabic Multi-Genre Corpus Diacritization</a></div><div class="wp-workCard_item"><span>Qatar Foundation Annual Research Conference Proceedings Volume 2016 Issue 1</span><span>, 2016</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">One of the characteristics of writing in Modern Standard Arabic (MSA) is that the commonly used o...</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">One of the characteristics of writing in Modern Standard Arabic (MSA) is that the commonly used orthography is mostly consonantal and does not provide full vocalization of the text. It sometimes includes optional diacritical marks (henceforth, diacritics or vowels).Arabic script consists of two classes of symbols: letters and diacritics. Letters comprise long vowels such as A, y, w as well as consonants. Diacritics on the other hand comprise short vowels, gemination markers, nunation markers, as well as other markers (such as hamza, the glottal stop which appears in conjunction with a small number of letters, dots on letters, elongation and emphatic markers) which in all, if present, render a more or less exact precise reading of a word. In this study, we are mostly addressing three types of diacritical marks: short vowels, nunation, and shadda (gemination).Diacritics are extremely useful for text readability and understanding. 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It refers to the phenomeno...</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">Language ambiguity is an inherent characteristic of natural languages. It refers to the phenomenon where an instance can be interpreted in multiple ways. Ambiguity is at the core of the problems faced by natural language processing applications (Obeid et al. 2013). Although humans have the ability to resolve such ambiguity based on their prior knowledge and context, there are instances (sentences, words,... etc) that require multiple readings to resolve it within a context (Hawwari et al. 2013; Diab et al. 2008). The problem of natural language ambiguity is further exacerbated by conventional orthographic decisions where not all phonemes are explicitly represented (Maamouri et al. 2010; Maamouri et al. 2012). Arabic standard orthography is one of these languages that is underspecified for some of the characters such as short vowels, gemination, glottal stops, etc which are collectively represented as diacritics (Zaghouani et al. 2012; Zaghouani et al. 2016). Most typical text in Ara...</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="118332106"><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="118332106"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 118332106; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=118332106]").text(description); $(".js-view-count[data-work-id=118332106]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 118332106; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='118332106']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 118332106, 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" } } $('.js-work-strip[data-work-id=118332106]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":118332106,"title":"Building a Rich Lexical Resource for Standard Arabic","translated_title":"","metadata":{"abstract":"Language ambiguity is an inherent characteristic of natural languages. 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Most typical text in Ara...","publisher":"Hamad bin Khalifa University Press (HBKU Press)","publication_date":{"day":null,"month":null,"year":2018,"errors":{}},"publication_name":"Qatar Foundation Annual Research Conference Proceedings Volume 2018 Issue 4"},"translated_abstract":"Language ambiguity is an inherent characteristic of natural languages. It refers to the phenomenon where an instance can be interpreted in multiple ways. Ambiguity is at the core of the problems faced by natural language processing applications (Obeid et al. 2013). Although humans have the ability to resolve such ambiguity based on their prior knowledge and context, there are instances (sentences, words,... etc) that require multiple readings to resolve it within a context (Hawwari et al. 2013; Diab et al. 2008). The problem of natural language ambiguity is further exacerbated by conventional orthographic decisions where not all phonemes are explicitly represented (Maamouri et al. 2010; Maamouri et al. 2012). Arabic standard orthography is one of these languages that is underspecified for some of the characters such as short vowels, gemination, glottal stops, etc which are collectively represented as diacritics (Zaghouani et al. 2012; Zaghouani et al. 2016). Most typical text in Ara...","internal_url":"https://www.academia.edu/118332106/Building_a_Rich_Lexical_Resource_for_Standard_Arabic","translated_internal_url":"","created_at":"2024-04-30T07:31:02.714-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32411980,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Building_a_Rich_Lexical_Resource_for_Standard_Arabic","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":32411980,"first_name":"Mona","middle_initials":null,"last_name":"Diab","page_name":"MDiab","domain_name":"gwu","created_at":"2015-06-21T18:43:17.283-07:00","display_name":"Mona Diab","url":"https://gwu.academia.edu/MDiab"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":3324,"name":"Arabic","url":"https://www.academia.edu/Documents/in/Arabic"}],"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="118332105"><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/118332105/Unsupervised_Word_Mapping_Using_Structural_Similarities_in_Monolingual_Embeddings"><img alt="Research paper thumbnail of Unsupervised Word Mapping Using Structural Similarities in Monolingual Embeddings" class="work-thumbnail" src="https://attachments.academia-assets.com/113984324/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/118332105/Unsupervised_Word_Mapping_Using_Structural_Similarities_in_Monolingual_Embeddings">Unsupervised Word Mapping Using Structural Similarities in Monolingual Embeddings</a></div><div class="wp-workCard_item"><span>Transactions of the Association for Computational Linguistics</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Most existing methods for automatic bilingual dictionary induction rely on prior alignments betwe...</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">Most existing methods for automatic bilingual dictionary induction rely on prior alignments between the source and target languages, such as parallel corpora or seed dictionaries. For many language pairs, such supervised alignments are not readily available. We propose an unsupervised approach for learning a bilingual dictionary for a pair of languages given their independently-learned monolingual word embeddings. The proposed method exploits local and global structures in monolingual vector spaces to align them such that similar words are mapped to each other. We show empirically that the performance of bilingual correspondents that are learned using our proposed unsupervised method is comparable to that of using supervised bilingual correspondents from a seed dictionary.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="ad11e7a413311d938d3a6e332ff0ba02" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":113984324,"asset_id":118332105,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/113984324/download_file?st=MTczMjQ5NDcwNiw4LjIyMi4yMDguMTQ2&st=MTczMjQ5NDcwNSw4LjIyMi4yMDguMTQ2&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="118332105"><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="118332105"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 118332105; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=118332105]").text(description); $(".js-view-count[data-work-id=118332105]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 118332105; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='118332105']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 118332105, 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: "ad11e7a413311d938d3a6e332ff0ba02" } } $('.js-work-strip[data-work-id=118332105]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":118332105,"title":"Unsupervised Word Mapping Using Structural Similarities in Monolingual Embeddings","translated_title":"","metadata":{"abstract":"Most existing methods for automatic bilingual dictionary induction rely on prior alignments between the source and target languages, such as parallel corpora or seed dictionaries. 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We present several models of semantic similarity, based on diering representational assumptions, and investigate their properties via comparison with human ratings of verb similarity. The results oer insight into the bases for human similarity judgments and provide a testbed for further investigation of the interactions among syn- tactic properties, semantic structure, and semantic con- tent. Introduction The way we model semantic similarity is closely tied to our understanding of how linguistic representations are acquired and used. Some models of similarity, such as Tversky&#39;s (1977), assume an explicit set of features over which a similarity measure can be computed, a...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="5cb511f45119da671ee1d58c24e4cb5e" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":113984307,"asset_id":118332009,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/113984307/download_file?st=MTczMjQ5NDcwNiw4LjIyMi4yMDguMTQ2&st=MTczMjQ5NDcwNiw4LjIyMi4yMDguMTQ2&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="118332009"><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="118332009"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 118332009; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=118332009]").text(description); $(".js-view-count[data-work-id=118332009]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 118332009; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='118332009']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 118332009, 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: "5cb511f45119da671ee1d58c24e4cb5e" } } $('.js-work-strip[data-work-id=118332009]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":118332009,"title":"Measuring Verb Similarity - eScholarship","translated_title":"","metadata":{"abstract":"Measuring Verb Similarity Philip Resnik and Mona Diab Department of Linguistics and Institute for Advanced Computer Studies University of Maryland College Park, MD USA f resnik,mdiab g @umiacs.umd.edu Abstract The way we model semantic similarity is closely tied to our understanding of linguistic representations. We present several models of semantic similarity, based on diering representational assumptions, and investigate their properties via comparison with human ratings of verb similarity. The results oer insight into the bases for human similarity judgments and provide a testbed for further investigation of the interactions among syn- tactic properties, semantic structure, and semantic con- tent. Introduction The way we model semantic similarity is closely tied to our understanding of how linguistic representations are acquired and used. Some models of similarity, such as Tversky\u0026#39;s (1977), assume an explicit set of features over which a similarity measure can be computed, a...","publication_date":{"day":null,"month":null,"year":2000,"errors":{}}},"translated_abstract":"Measuring Verb Similarity Philip Resnik and Mona Diab Department of Linguistics and Institute for Advanced Computer Studies University of Maryland College Park, MD USA f resnik,mdiab g @umiacs.umd.edu Abstract The way we model semantic similarity is closely tied to our understanding of linguistic representations. We present several models of semantic similarity, based on diering representational assumptions, and investigate their properties via comparison with human ratings of verb similarity. The results oer insight into the bases for human similarity judgments and provide a testbed for further investigation of the interactions among syn- tactic properties, semantic structure, and semantic con- tent. Introduction The way we model semantic similarity is closely tied to our understanding of how linguistic representations are acquired and used. 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