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class="profile--tab_heading_container">Papers by Michael Flor</h3></div><div class="js-work-strip profile--work_container" data-work-id="120067089"><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/120067089/Three_Studies_on_Predicting_Word_Concreteness_with_Embedding_Vectors"><img alt="Research paper thumbnail of Three Studies on Predicting Word Concreteness with Embedding Vectors" class="work-thumbnail" src="https://attachments.academia-assets.com/115337290/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/120067089/Three_Studies_on_Predicting_Word_Concreteness_with_Embedding_Vectors">Three Studies on Predicting Word Concreteness with Embedding Vectors</a></div><div class="wp-workCard_item"><span>The Workshop on Cognitive Aspects of the Lexicon </span><span>, 2024</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Human-assigned concreteness ratings for words are commonly used in psycholinguistic and computati...</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">Human-assigned concreteness ratings for words are commonly used in psycholinguistic and computational linguistic studies. Previous research has shown that such ratings can be modeled and extrapolated by using dense word-embedding representations. However, due to rater disagreement, considerable amounts of human ratings in published datasets are not reliable. We investigate how such unreliable data influences modeling of concreteness with word embeddings. Study 1 compares fourteen embedding models over three datasets of concreteness ratings, showing that most models achieve high correlations with human ratings, and exhibit low error rates on predictions. Study 2 investigates how exclusion of the less reliable ratings influences the modeling results. It indicates that improved results can be achieved when data is cleaned. Study 3 adds additional conditions over those of study 2 and indicates that the improved results hold only for the cleaned data, and that in the general case removing the less reliable data points is not useful.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="ae22b309ae55e0be68210490bc369c0a" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":115337290,"asset_id":120067089,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/115337290/download_file?st=MTczMjQ5NDE3OSw4LjIyMi4yMDguMTQ2&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="120067089"><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="120067089"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 120067089; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=120067089]").text(description); $(".js-view-count[data-work-id=120067089]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 120067089; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='120067089']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 120067089, 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: "ae22b309ae55e0be68210490bc369c0a" } } $('.js-work-strip[data-work-id=120067089]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":120067089,"title":"Three Studies on Predicting Word Concreteness with Embedding Vectors","translated_title":"","metadata":{"abstract":"Human-assigned concreteness ratings for words are commonly used in psycholinguistic and computational linguistic studies. 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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="116602324"><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/116602324/Mapping_of_American_English_vocabulary_by_grade_levels"><img alt="Research paper thumbnail of Mapping of American English vocabulary by grade levels" class="work-thumbnail" src="https://attachments.academia-assets.com/112686421/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/116602324/Mapping_of_American_English_vocabulary_by_grade_levels">Mapping of American English vocabulary by grade levels</a></div><div class="wp-workCard_item"><span> ITL - International Journal of Applied Linguistics </span><span>, 2024</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We describe a large-scale effort to map English-language vocabulary by U.S. school grade levels. ...</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">We describe a large-scale effort to map English-language vocabulary by U.S. school grade levels. Our motivation is to rapidly expand graded vocabulary resources for work with native English speakers in the USA, while taking into consideration school-related influences rather than relying on just the corpus-frequency approaches. We report on the initial effort of data collection, with mapping of about 22K word forms. We provide comparisons of this mapping to some other recent vocabulary mapping efforts, such as age-of-acquisition. We then describe the efforts to automatically expand this resource by using linguistically motivated variables and corpus-based methods. Our current resource maps more than 126K English word forms to US school grade levels. We also compare a subset of our L1 mapped data to English L2 vocabulary levels, as expressed on the CEFR scale, and find that there is a considerable overlap in the order of vocabulary learning in L1 and</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="b5fb58bf5425f07079e39fbdd28fc212" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":112686421,"asset_id":116602324,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/112686421/download_file?st=MTczMjQ5NDE3OSw4LjIyMi4yMDguMTQ2&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="116602324"><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="116602324"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 116602324; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=116602324]").text(description); $(".js-view-count[data-work-id=116602324]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 116602324; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='116602324']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 116602324, 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: "b5fb58bf5425f07079e39fbdd28fc212" } } $('.js-work-strip[data-work-id=116602324]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":116602324,"title":"Mapping of American English vocabulary by grade levels","translated_title":"","metadata":{"doi":"10.1075/itl.22025.flo","abstract":"We describe a large-scale effort to map English-language vocabulary by U.S. school grade levels. 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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="82813749"><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/82813749/Exploring_Automated_Classification_Approaches_to_Advance_the_Assessment_of_Collaborative_Problem_Solving_Skills"><img alt="Research paper thumbnail of Exploring Automated Classification Approaches to Advance the Assessment of Collaborative Problem Solving Skills" 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/82813749/Exploring_Automated_Classification_Approaches_to_Advance_the_Assessment_of_Collaborative_Problem_Solving_Skills">Exploring Automated Classification Approaches to Advance the Assessment of Collaborative Problem Solving Skills</a></div><div class="wp-workCard_item"><span>Journal of Intelligence</span><span>, Jul 4, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Competency in skills associated with collaborative problem solving (CPS) is critical for many con...</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">Competency in skills associated with collaborative problem solving (CPS) is critical for many contexts, including school, the workplace, and the military. Innovative approaches for assessing individuals’ CPS competency are necessary, as traditional assessment types such as multiple -choice items are not well suited for such a process-oriented competency. In a move to computer-based environments to support CPS assessment, innovative computational approaches are also needed to understand individuals’ CPS behaviors. In the current study, we describe the use of a simulation-based task on electronics concepts as an environment for higher education students to display evidence of their CPS competency. We further describe computational linguistic methods for automatically characterizing students’ display of various CPS skills in the task. Comparisons between such an automated approach and an approach based on human annotation to characterize student CPS behaviors revealed above average agreement. These results give credence to the potential for automated approaches to help advance the assessment of CPS and to circumvent the time-intensive human annotation approaches that are typically used in these contexts.</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="82813749"><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="82813749"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 82813749; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=82813749]").text(description); $(".js-view-count[data-work-id=82813749]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 82813749; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='82813749']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 82813749, 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" } } $('.js-work-strip[data-work-id=82813749]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":82813749,"title":"Exploring Automated Classification Approaches to Advance the Assessment of Collaborative Problem Solving Skills","translated_title":"","metadata":{"doi":"10.3390/jintelligence10030039","abstract":"Competency in skills associated with collaborative problem solving (CPS) is critical for many contexts, including school, the workplace, and the military. Innovative approaches for assessing individuals’ CPS competency are necessary, as traditional assessment types such as multiple -choice items are not well suited for such a process-oriented competency. In a move to computer-based environments to support CPS assessment, innovative computational approaches are also needed to understand individuals’ CPS behaviors. In the current study, we describe the use of a simulation-based task on electronics concepts as an environment for higher education students to display evidence of their CPS competency. We further describe computational linguistic methods for automatically characterizing students’ display of various CPS skills in the task. Comparisons between such an automated approach and an approach based on human annotation to characterize student CPS behaviors revealed above average agreement. These results give credence to the potential for automated approaches to help advance the assessment of CPS and to circumvent the time-intensive human annotation approaches that are typically used in these contexts.","publication_date":{"day":4,"month":7,"year":2022,"errors":{}},"publication_name":"Journal of Intelligence"},"translated_abstract":"Competency in skills associated with collaborative problem solving (CPS) is critical for many contexts, including school, the workplace, and the military. Innovative approaches for assessing individuals’ CPS competency are necessary, as traditional assessment types such as multiple -choice items are not well suited for such a process-oriented competency. In a move to computer-based environments to support CPS assessment, innovative computational approaches are also needed to understand individuals’ CPS behaviors. In the current study, we describe the use of a simulation-based task on electronics concepts as an environment for higher education students to display evidence of their CPS competency. We further describe computational linguistic methods for automatically characterizing students’ display of various CPS skills in the task. Comparisons between such an automated approach and an approach based on human annotation to characterize student CPS behaviors revealed above average agreement. These results give credence to the potential for automated approaches to help advance the assessment of CPS and to circumvent the time-intensive human annotation approaches that are typically used in these contexts.","internal_url":"https://www.academia.edu/82813749/Exploring_Automated_Classification_Approaches_to_Advance_the_Assessment_of_Collaborative_Problem_Solving_Skills","translated_internal_url":"","created_at":"2022-07-08T12:59:41.987-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":3199073,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Exploring_Automated_Classification_Approaches_to_Advance_the_Assessment_of_Collaborative_Problem_Solving_Skills","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":3199073,"first_name":"Michael","middle_initials":null,"last_name":"Flor","page_name":"MichaelFlor","domain_name":"ets","created_at":"2013-01-24T00:10:26.300-08:00","display_name":"Michael Flor","url":"https://ets.academia.edu/MichaelFlor"},"attachments":[],"research_interests":[{"id":94,"name":"Discourse Analysis","url":"https://www.academia.edu/Documents/in/Discourse_Analysis"},{"id":4828,"name":"Collaboration","url":"https://www.academia.edu/Documents/in/Collaboration"},{"id":8679,"name":"Computer Supported Collaborative Learning (CSCL)","url":"https://www.academia.edu/Documents/in/Computer_Supported_Collaborative_Learning_CSCL_"},{"id":3521305,"name":"Automated Text Classification","url":"https://www.academia.edu/Documents/in/Automated_Text_Classification"}],"urls":[{"id":22017757,"url":"https://www.mdpi.com/2079-3200/10/3/39"}]}, 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="79783863"><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/79783863/Towards_automatic_annotation_of_collaborative_problem_solving_skills_in_technology_enhanced_environments"><img alt="Research paper thumbnail of Towards automatic annotation of collaborative problem-solving skills in technology-enhanced environments" class="work-thumbnail" src="https://attachments.academia-assets.com/94847882/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/79783863/Towards_automatic_annotation_of_collaborative_problem_solving_skills_in_technology_enhanced_environments">Towards automatic annotation of collaborative problem-solving skills in technology-enhanced environments</a></div><div class="wp-workCard_item"><span>Journal of Computer Assisted Learning</span><span>, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Objective: We explore possibilities for automated annotation of actions in collaborative-teams, ...</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">Objective: We explore possibilities for automated annotation of actions in <br />collaborative-teams, especially chat messages. We evaluate two approaches that <br />employ machine learning for automated classification of CPS events. <br />Method: Data were collected from engineering, physics and electronics students' <br />participation in a simulation-based task on electronics concepts, in which participants <br />communicated via text-chat messages. All task activities were logged and time <br />stamped. Data have been manually classified for the CPS skills, using an ontology <br />that includes both social and cognitive dimensions. In this article, we describe computational linguistic methods for automatically classifying the CPS skills from logged data, with a view towards automating CPS assessments. <br />Results: We applied two machine learning methods to our data. A Naïve Bayes classifier has been previously used in CPS research, but it is only moderately successful on our data. We also present a k-nearest-neighbours (kNN) classifier that uses distributional semantic models for measuring text similarity. This classifier shows strong agreement between automated and human annotations. The study also demonstrates that automatic spelling correction and slang normalization of chat texts are useful for accurate automated annotation.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="abc72262b35cbc2ace3413a8845369fe" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":94847882,"asset_id":79783863,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/94847882/download_file?st=MTczMjQ5NDE3OSw4LjIyMi4yMDguMTQ2&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="79783863"><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="79783863"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79783863; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=79783863]").text(description); $(".js-view-count[data-work-id=79783863]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 79783863; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='79783863']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 79783863, 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: "abc72262b35cbc2ace3413a8845369fe" } } $('.js-work-strip[data-work-id=79783863]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":79783863,"title":"Towards automatic annotation of collaborative problem-solving skills in technology-enhanced environments","translated_title":"","metadata":{"doi":"10.1111/jcal.12689","abstract":"Objective: We explore possibilities for automated annotation of actions in\r\ncollaborative-teams, especially chat messages. We evaluate two approaches that\r\nemploy machine learning for automated classification of CPS events.\r\nMethod: Data were collected from engineering, physics and electronics students'\r\nparticipation in a simulation-based task on electronics concepts, in which participants\r\ncommunicated via text-chat messages. All task activities were logged and time\r\nstamped. Data have been manually classified for the CPS skills, using an ontology\r\nthat includes both social and cognitive dimensions. In this article, we describe computational linguistic methods for automatically classifying the CPS skills from logged data, with a view towards automating CPS assessments.\r\nResults: We applied two machine learning methods to our data. A Naïve Bayes classifier has been previously used in CPS research, but it is only moderately successful on our data. We also present a k-nearest-neighbours (kNN) classifier that uses distributional semantic models for measuring text similarity. This classifier shows strong agreement between automated and human annotations. The study also demonstrates that automatic spelling correction and slang normalization of chat texts are useful for accurate automated annotation.","publication_date":{"day":null,"month":null,"year":2022,"errors":{}},"publication_name":"Journal of Computer Assisted Learning"},"translated_abstract":"Objective: We explore possibilities for automated annotation of actions in\r\ncollaborative-teams, especially chat messages. We evaluate two approaches that\r\nemploy machine learning for automated classification of CPS events.\r\nMethod: Data were collected from engineering, physics and electronics students'\r\nparticipation in a simulation-based task on electronics concepts, in which participants\r\ncommunicated via text-chat messages. All task activities were logged and time\r\nstamped. Data have been manually classified for the CPS skills, using an ontology\r\nthat includes both social and cognitive dimensions. In this article, we describe computational linguistic methods for automatically classifying the CPS skills from logged data, with a view towards automating CPS assessments.\r\nResults: We applied two machine learning methods to our data. A Naïve Bayes classifier has been previously used in CPS research, but it is only moderately successful on our data. We also present a k-nearest-neighbours (kNN) classifier that uses distributional semantic models for measuring text similarity. This classifier shows strong agreement between automated and human annotations. 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We represent the amount of association in a text using word association profile ‐ a distribution of pointwise mutual information between all pairs of content word types in a text. We use the average of the distribution, which we term lexical tightness, as a single measure of the amount of association in a text. We show that the lexical tightness of humancomposed texts is higher than that of the machine translated materials; human references are tighter than machine translations, and better MT systems produce lexically tighter translations. While the phenomenon of the loss of associative texture has been theoretically predicted by translation scholars, we present a measure capable of quantifying the extent of this phenomenon.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f661940b2fce063835e98c9cba9ba38b" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":77085349,"asset_id":65530897,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/77085349/download_file?st=MTczMjQ5NDE3OSw4LjIyMi4yMDguMTQ2&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="65530897"><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="65530897"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 65530897; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=65530897]").text(description); $(".js-view-count[data-work-id=65530897]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 65530897; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='65530897']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 65530897, 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: "f661940b2fce063835e98c9cba9ba38b" } } $('.js-work-strip[data-work-id=65530897]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":65530897,"title":"Associative Texture Is Lost In Translation","translated_title":"","metadata":{"abstract":"We present a suggestive finding regarding the loss of associative texture in the process of machine translation, using comparisons between (a) original and backtranslated texts, (b) reference and system translations, and (c) better and worse MT systems. 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class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/65530896/On_using_context_for_automatic_correction_of_non_word_misspellings_in_student_essays"><img alt="Research paper thumbnail of On using context for automatic correction of non-word misspellings in student essays" class="work-thumbnail" src="https://attachments.academia-assets.com/77085580/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/65530896/On_using_context_for_automatic_correction_of_non_word_misspellings_in_student_essays">On using context for automatic correction of non-word misspellings in student essays</a></div><div class="wp-workCard_item"><span>The 7th Workshop on Innovative Use of NLP for Building Educational Applications, 2012</span><span>, 2012</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In this paper we present a new spell-checking system that utilizes contextual information for aut...</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 this paper we present a new spell-checking system that utilizes contextual information for automatic correction of non-word misspellings. The system is evaluated with a large corpus of essays written by native and non-native speakers of English to the writing prompts of high-stakes standardized tests (TOEFL® and GRE®). We also present comparative evaluations with Aspell and the speller from Microsoft Office 2007. Using context-informed re-ranking of candidate suggestions, our system exhibits superior error-correction results overall and also corrects errors generated by non-native English writers with almost same rate of success as it does for writers who are native English speakers.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="1830e459a32c91be0b147b6515ba47fa" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":77085580,"asset_id":65530896,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/77085580/download_file?st=MTczMjQ5NDE3OSw4LjIyMi4yMDguMTQ2&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="65530896"><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="65530896"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 65530896; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=65530896]").text(description); $(".js-view-count[data-work-id=65530896]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 65530896; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='65530896']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 65530896, 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: "1830e459a32c91be0b147b6515ba47fa" } } $('.js-work-strip[data-work-id=65530896]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":65530896,"title":"On using context for automatic correction of non-word misspellings in student essays","translated_title":"","metadata":{"abstract":"In this paper we present a new spell-checking system that utilizes contextual information for automatic correction of non-word misspellings. 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with automatic spelling correction" 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/65530895/Producing_an_annotated_corpus_with_automatic_spelling_correction">Producing an annotated corpus with automatic spelling correction</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper describes ConSpel, a software system for automatic detection and correction of non-wor...</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 describes ConSpel, a software system for automatic detection and correction of non-word misspellings. We also present an ongoing research project for constructing an ETS (Educational Testing Service) Spelling Corpus. The corpus consists of essays written by native and non-native speakers of English to the writing prompts of TOEFL® and GRE® tests. Essays are annotated for misspellings by trained annotators, using a semi-automated methodology. An evaluation of the ConSpel system was conducted, using the data from the completed phase of the annotation project. The ConSpel system achieves above 95% accuracy in error detection. The evaluation also indicates that an advanced correction algorithm, which takes into account the local context of misspellings, achieves correction accuracy of 77% and consistently outperforms a baseline context-blind approach.</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="65530895"><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="65530895"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 65530895; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=65530895]").text(description); $(".js-view-count[data-work-id=65530895]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 65530895; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='65530895']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 65530895, 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" } } $('.js-work-strip[data-work-id=65530895]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":65530895,"title":"Producing an annotated corpus with automatic spelling correction","translated_title":"","metadata":{"abstract":"This paper describes ConSpel, a software system for automatic detection and correction of non-word misspellings. 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The evaluation also indicates that an advanced correction algorithm, which takes into account the local context of misspellings, achieves correction accuracy of 77% and consistently outperforms a baseline context-blind approach.","internal_url":"https://www.academia.edu/65530895/Producing_an_annotated_corpus_with_automatic_spelling_correction","translated_internal_url":"","created_at":"2021-12-22T09:12:37.566-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":3199073,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Producing_an_annotated_corpus_with_automatic_spelling_correction","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":3199073,"first_name":"Michael","middle_initials":null,"last_name":"Flor","page_name":"MichaelFlor","domain_name":"ets","created_at":"2013-01-24T00:10:26.300-08:00","display_name":"Michael Flor","url":"https://ets.academia.edu/MichaelFlor"},"attachments":[],"research_interests":[],"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="65530894"><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/65530894/Argumentation_Relevant_Metaphors_in_Test_Taker_Essays"><img alt="Research paper thumbnail of Argumentation-Relevant Metaphors in Test-Taker Essays" class="work-thumbnail" src="https://attachments.academia-assets.com/77085348/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/65530894/Argumentation_Relevant_Metaphors_in_Test_Taker_Essays">Argumentation-Relevant Metaphors in Test-Taker Essays</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This article discusses metaphor annotation in a corpus of argumentative essays written by test-ta...</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 article discusses metaphor annotation in a corpus of argumentative essays written by test-takers during a standardized examination for graduate school admission. The quality of argumentation being the focus of the project, we developed a metaphor annotation protocol that targets metaphors that are relevant for the writer’s arguments. The reliability of the protocol is =0.58, on a set of 116 essays (the total of about 30K content-word tokens). We found a moderate-to-strong correlation (r=0.51-0.57) between the percentage of metaphorically used words in an essay and the writing quality score. We also describe encouraging findings regarding the potential of metaphor identification to contribute to automated scoring of essays.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="784201e63f0ee98569c6e88608385b92" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":77085348,"asset_id":65530894,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/77085348/download_file?st=MTczMjQ5NDE3OSw4LjIyMi4yMDguMTQ2&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="65530894"><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="65530894"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 65530894; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=65530894]").text(description); $(".js-view-count[data-work-id=65530894]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 65530894; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='65530894']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 65530894, 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: "784201e63f0ee98569c6e88608385b92" } } $('.js-work-strip[data-work-id=65530894]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":65530894,"title":"Argumentation-Relevant Metaphors in Test-Taker Essays","translated_title":"","metadata":{"abstract":"This article discusses metaphor annotation in a corpus of argumentative essays written by test-takers during a standardized examination for graduate school admission. The quality of argumentation being the focus of the project, we developed a metaphor annotation protocol that targets metaphors that are relevant for the writer’s arguments. The reliability of the protocol is =0.58, on a set of 116 essays (the total of about 30K content-word tokens). We found a moderate-to-strong correlation (r=0.51-0.57) between the percentage of metaphorically used words in an essay and the writing quality score. We also describe encouraging findings regarding the potential of metaphor identification to contribute to automated scoring of essays.","publication_date":{"day":null,"month":null,"year":2013,"errors":{}}},"translated_abstract":"This article discusses metaphor annotation in a corpus of argumentative essays written by test-takers during a standardized examination for graduate school admission. 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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="65530753"><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/65530753/Developing_an_e_rater_Advisory_to_detect_Babel_generated_essays"><img alt="Research paper thumbnail of Developing an e-rater Advisory to detect Babel-generated essays" class="work-thumbnail" src="https://attachments.academia-assets.com/77085300/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/65530753/Developing_an_e_rater_Advisory_to_detect_Babel_generated_essays">Developing an e-rater Advisory to detect Babel-generated essays</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Background: It is important for developers of automated scoring systems to ensure that their syst...</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">Background: It is important for developers of automated scoring systems to ensure that their systems are as fair and valid as possible. This commitment means evaluating the performance of these systems in light of construct-irrelevant response strategies. The enhancement of systems to detect and deal with these kinds of strategies is often an iterative process, whereby as new strategies come to light they need to be evaluated and effective mechanisms built into the automated scoring systems to handle them. In this paper, we focus on the Babel system, which automatically generates semantically incohesive essays. We expect that these essays may unfairly receive high scores from automated scoring engines despite essentially being nonsense. Literature Review: We discuss literature related to gaming of automated scoring systems. One reason that Babel essays are so easy to identify as nonsense by human readers is that they lack any semantic cohesion. Therefore, we also discuss some litera...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="9ab3c10cc0e4987c2e0e28804c68eca6" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":77085300,"asset_id":65530753,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/77085300/download_file?st=MTczMjQ5NDE3OSw4LjIyMi4yMDguMTQ2&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="65530753"><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="65530753"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 65530753; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=65530753]").text(description); $(".js-view-count[data-work-id=65530753]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 65530753; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='65530753']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 65530753, 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: "9ab3c10cc0e4987c2e0e28804c68eca6" } } $('.js-work-strip[data-work-id=65530753]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":65530753,"title":"Developing an e-rater Advisory to detect Babel-generated essays","translated_title":"","metadata":{"abstract":"Background: It is important for developers of automated scoring systems to ensure that their systems are as fair and valid as possible. This commitment means evaluating the performance of these systems in light of construct-irrelevant response strategies. The enhancement of systems to detect and deal with these kinds of strategies is often an iterative process, whereby as new strategies come to light they need to be evaluated and effective mechanisms built into the automated scoring systems to handle them. In this paper, we focus on the Babel system, which automatically generates semantically incohesive essays. We expect that these essays may unfairly receive high scores from automated scoring engines despite essentially being nonsense. Literature Review: We discuss literature related to gaming of automated scoring systems. One reason that Babel essays are so easy to identify as nonsense by human readers is that they lack any semantic cohesion. Therefore, we also discuss some litera...","publication_date":{"day":null,"month":null,"year":2018,"errors":{}}},"translated_abstract":"Background: It is important for developers of automated scoring systems to ensure that their systems are as fair and valid as possible. This commitment means evaluating the performance of these systems in light of construct-irrelevant response strategies. The enhancement of systems to detect and deal with these kinds of strategies is often an iterative process, whereby as new strategies come to light they need to be evaluated and effective mechanisms built into the automated scoring systems to handle them. In this paper, we focus on the Babel system, which automatically generates semantically incohesive essays. We expect that these essays may unfairly receive high scores from automated scoring engines despite essentially being nonsense. Literature Review: We discuss literature related to gaming of automated scoring systems. One reason that Babel essays are so easy to identify as nonsense by human readers is that they lack any semantic cohesion. 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analytics","url":"https://www.academia.edu/Documents/in/writing_analytics"}],"urls":[{"id":15542019,"url":"https://wac.colostate.edu/docs/jwa/vol2/cahill.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="49252725"><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/49252725/Systems_and_methods_for_automatic_generation_of_questions_from_text"><img alt="Research paper thumbnail of Systems and methods for automatic generation of questions from text." class="work-thumbnail" src="https://attachments.academia-assets.com/67636432/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/49252725/Systems_and_methods_for_automatic_generation_of_questions_from_text">Systems and methods for automatic generation of questions from text.</a></div><div class="wp-workCard_item"><span>USPTO</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Computer-implemented systems and methods are described herein for automatically generating questi...</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">Computer-implemented systems and methods are described herein for automatically generating questions from text. Text including one or more sentences is received. A sentence, comprising a predicate and one or more arguments associated with the predicate, is parsed from the text. Semantic role labels are assigned to the one or more arguments associated with the predicate. One or more questions are automatically generated relating to the predicate based on the assigned semantic role labels. Each answer to the generated questions is one of the one or more arguments associated with the predicate.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="ad66c6a314c001f8e2ac630d28b9a97d" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":67636432,"asset_id":49252725,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/67636432/download_file?st=MTczMjQ5NDE3OSw4LjIyMi4yMDguMTQ2&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="49252725"><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="49252725"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 49252725; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=49252725]").text(description); $(".js-view-count[data-work-id=49252725]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 49252725; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='49252725']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 49252725, 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: "ad66c6a314c001f8e2ac630d28b9a97d" } } $('.js-work-strip[data-work-id=49252725]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":49252725,"title":"Systems and methods for automatic generation of questions from text.","translated_title":"","metadata":{"abstract":"Computer-implemented systems and methods are described herein for automatically generating questions from text. 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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="43733778"><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/43733778/Emotion_Arcs_of_Student_Narratives"><img alt="Research paper thumbnail of Emotion Arcs of Student Narratives" class="work-thumbnail" src="https://attachments.academia-assets.com/64043676/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/43733778/Emotion_Arcs_of_Student_Narratives">Emotion Arcs of Student Narratives</a></div><div class="wp-workCard_item"><span>Proceedings of the First Joint Workshop on Narrative Understanding, Storylines, and Events</span><span>, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper studies emotion arcs in student narratives. We construct emotion arcs based on event 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">This paper studies emotion arcs in student narratives. We construct emotion arcs based on event affect and implied sentiments, which correspond to plot elements in the story. We show that student narratives can show elements of plot structure in their emotion arcs and that properties of these arcs can be useful indicators of narrative quality. We build a system and perform analysis to show that our arc-based features are complementary to previously studied sentiment features in this area.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="41f57ec42a6f85ed9c990ac01ee0053c" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":64043676,"asset_id":43733778,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/64043676/download_file?st=MTczMjQ5NDE3OSw4LjIyMi4yMDguMTQ2&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="43733778"><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="43733778"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 43733778; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=43733778]").text(description); $(".js-view-count[data-work-id=43733778]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 43733778; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='43733778']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 43733778, 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: "41f57ec42a6f85ed9c990ac01ee0053c" } } $('.js-work-strip[data-work-id=43733778]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":43733778,"title":"Emotion Arcs of Student Narratives","translated_title":"","metadata":{"abstract":"This paper studies emotion arcs in student narratives. 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href="https://www.academia.edu/43733711/Go_Figure_Multi_task_transformer_based_architecture_for_metaphor_detection_using_idioms_ETS_team_in_2020_metaphor_shared_task"><img alt="Research paper thumbnail of Go Figure! Multi-task transformer-based architecture for metaphor detection using idioms: ETS team in 2020 metaphor shared task" class="work-thumbnail" src="https://attachments.academia-assets.com/64043619/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/43733711/Go_Figure_Multi_task_transformer_based_architecture_for_metaphor_detection_using_idioms_ETS_team_in_2020_metaphor_shared_task">Go Figure! Multi-task transformer-based architecture for metaphor detection using idioms: ETS team in 2020 metaphor shared task</a></div><div class="wp-workCard_item"><span>Proceedings of the Second Workshop on Figurative Language Processing</span><span>, Jul 9, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper describes the ETS entry to the 2020 Metaphor Detection shared task. Our contribution c...</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 describes the ETS entry to the 2020 Metaphor Detection shared task. Our contribution consists of a sequence of experiments using BERT, starting with a baseline, strengthening it by spell-correcting the TOEFL corpus , followed by a multi-task learning setting , where one of the tasks is the token-level metaphor classification as per the shared task, while the other is meant to provide additional training that we hypothesized to be relevant to the main task. In one case, out-of-domain data manually annotated for metaphor is used for the auxiliary task; in the other case, in-domain data automatically annotated for idioms is used for the auxiliary task. Both multi-task experiments yield promising results.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="059128da26fc7f2d5343318f3310b663" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":64043619,"asset_id":43733711,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/64043619/download_file?st=MTczMjQ5NDE3OSw4LjIyMi4yMDguMTQ2&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="43733711"><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="43733711"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 43733711; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=43733711]").text(description); $(".js-view-count[data-work-id=43733711]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 43733711; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='43733711']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 43733711, 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: "059128da26fc7f2d5343318f3310b663" } } $('.js-work-strip[data-work-id=43733711]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":43733711,"title":"Go Figure! 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In one case, out-of-domain data manually annotated for metaphor is used for the auxiliary task; in the other case, in-domain data automatically annotated for idioms is used for the auxiliary task. Both multi-task experiments yield promising results.","internal_url":"https://www.academia.edu/43733711/Go_Figure_Multi_task_transformer_based_architecture_for_metaphor_detection_using_idioms_ETS_team_in_2020_metaphor_shared_task","translated_internal_url":"","created_at":"2020-07-28T19:55:17.649-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":3199073,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":35407010,"work_id":43733711,"tagging_user_id":3199073,"tagged_user_id":null,"co_author_invite_id":7079985,"email":"x***2@ets.org","display_order":-2,"name":"Xianyang Chen","title":"Go Figure! 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We pres...</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 study explores the relation between lexical concreteness and narrative text quality. We present a methodology to quantitatively measure lexical concreteness of a text. We apply it to a corpus of student stories, scored according to writing evaluation rubrics. Lexical concreteness is weakly-to-moderately related to story quality, depending on story-type. The relation is mostly borne by adjectives and nouns, but also found for adverbs and verbs.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="a05a5667b7dbe609efea2ac12a2cf75c" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":60355929,"asset_id":40139495,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/60355929/download_file?st=MTczMjQ5NDE3OSw4LjIyMi4yMDguMTQ2&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="40139495"><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="40139495"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 40139495; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=40139495]").text(description); $(".js-view-count[data-work-id=40139495]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 40139495; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='40139495']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 40139495, 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: "a05a5667b7dbe609efea2ac12a2cf75c" } } $('.js-work-strip[data-work-id=40139495]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":40139495,"title":"Lexical concreteness in narrative","translated_title":"","metadata":{"abstract":"This study explores the relation between lexical concreteness and narrative text quality. 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The relation is mostly borne by adjectives and nouns, but also found for adverbs and verbs.","internal_url":"https://www.academia.edu/40139495/Lexical_concreteness_in_narrative","translated_internal_url":"","created_at":"2019-08-21T09:03:00.151-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":3199073,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":32925400,"work_id":40139495,"tagging_user_id":3199073,"tagged_user_id":null,"co_author_invite_id":6184186,"email":"s***n@ets.org","display_order":2,"name":"Swapna Somasundaran","title":"Lexical concreteness in narrative"}],"downloadable_attachments":[{"id":60355929,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/60355929/thumbnails/1.jpg","file_name":"Lexical_concreteness_in_narrative__2019_20190821-81645-19pboop.pdf","download_url":"https://www.academia.edu/attachments/60355929/download_file?st=MTczMjQ5NDE3OSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Lexical_concreteness_in_narrative.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/60355929/Lexical_concreteness_in_narrative__2019_20190821-81645-19pboop-libre.pdf?1566406235=\u0026response-content-disposition=attachment%3B+filename%3DLexical_concreteness_in_narrative.pdf\u0026Expires=1732474251\u0026Signature=N-PzOZOTgA7KpxomufM6HJ7kd9emvcmIBud71jD0QSPnzr4VV27X96JI1~RtNwn-SOF4awM1QuAhSfRF2dAQHkUaKN-tMNl1wlfmw-bT3xI2KCxvaY6nOvGL~bTCs5PcvWoVeoWH~7vB3xdInu0Gf7gtHE0nHM87W-1rzt8u3ZkG64XDuxWEjc8GSerEvyWRwiJL4moBo2xOehwQ5mkNpShtL7ukqpgz69oOlDZoC7eKXu5ppg~KT5nWOBPq878~aNTl01KL8fJy5B0Ua6~QWAugdwlyu3bINOd-64I3xWQht7EBhICBjJ48P8lpA1HUcolFQKD8gaiwPkg~MK6TjQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Lexical_concreteness_in_narrative","translated_slug":"","page_count":6,"language":"en","content_type":"Work","owner":{"id":3199073,"first_name":"Michael","middle_initials":null,"last_name":"Flor","page_name":"MichaelFlor","domain_name":"ets","created_at":"2013-01-24T00:10:26.300-08:00","display_name":"Michael Flor","url":"https://ets.academia.edu/MichaelFlor","email":"MzNUM0JZUkUwd0ErUzk1UE9BZFdCRXh5TVFmUW9YVzlvdUZBY3Z5VjVPWTM4aFVWY29oeFpXS3BROEhQei9XSy0tMllRM09FWUtYK2FndGFtbHI3UHRLdz09--2b0dea0622fe80808bd25eeafde2dffe19423c6a"},"attachments":[{"id":60355929,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/60355929/thumbnails/1.jpg","file_name":"Lexical_concreteness_in_narrative__2019_20190821-81645-19pboop.pdf","download_url":"https://www.academia.edu/attachments/60355929/download_file?st=MTczMjQ5NDE3OSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Lexical_concreteness_in_narrative.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/60355929/Lexical_concreteness_in_narrative__2019_20190821-81645-19pboop-libre.pdf?1566406235=\u0026response-content-disposition=attachment%3B+filename%3DLexical_concreteness_in_narrative.pdf\u0026Expires=1732474251\u0026Signature=N-PzOZOTgA7KpxomufM6HJ7kd9emvcmIBud71jD0QSPnzr4VV27X96JI1~RtNwn-SOF4awM1QuAhSfRF2dAQHkUaKN-tMNl1wlfmw-bT3xI2KCxvaY6nOvGL~bTCs5PcvWoVeoWH~7vB3xdInu0Gf7gtHE0nHM87W-1rzt8u3ZkG64XDuxWEjc8GSerEvyWRwiJL4moBo2xOehwQ5mkNpShtL7ukqpgz69oOlDZoC7eKXu5ppg~KT5nWOBPq878~aNTl01KL8fJy5B0Ua6~QWAugdwlyu3bINOd-64I3xWQht7EBhICBjJ48P8lpA1HUcolFQKD8gaiwPkg~MK6TjQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":2852,"name":"Narrative","url":"https://www.academia.edu/Documents/in/Narrative"},{"id":5513,"name":"Narratology","url":"https://www.academia.edu/Documents/in/Narratology"},{"id":6492,"name":"Storytelling","url":"https://www.academia.edu/Documents/in/Storytelling"},{"id":13769,"name":"Lexical Semantics","url":"https://www.academia.edu/Documents/in/Lexical_Semantics"},{"id":15674,"name":"Linguistics","url":"https://www.academia.edu/Documents/in/Linguistics"},{"id":18880,"name":"Narrative Analysis","url":"https://www.academia.edu/Documents/in/Narrative_Analysis"},{"id":19818,"name":"Narrative Theory","url":"https://www.academia.edu/Documents/in/Narrative_Theory"},{"id":49267,"name":"Computational Linguistics \u0026 NLP","url":"https://www.academia.edu/Documents/in/Computational_Linguistics_and_NLP"},{"id":332649,"name":"Concreteness","url":"https://www.academia.edu/Documents/in/Concreteness"}],"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="40139377"><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/40139377/A_Benchmark_Corpus_of_English_Misspellings_and_a_Minimally_supervised_Model_for_Spelling_Correction"><img alt="Research paper thumbnail of A Benchmark Corpus of English Misspellings and a Minimally-supervised Model for Spelling Correction" class="work-thumbnail" src="https://attachments.academia-assets.com/60355773/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/40139377/A_Benchmark_Corpus_of_English_Misspellings_and_a_Minimally_supervised_Model_for_Spelling_Correction">A Benchmark Corpus of English Misspellings and a Minimally-supervised Model for Spelling Correction</a></div><div class="wp-workCard_item"><span>Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Spelling correction has attracted a lot of attention in the NLP community. However, models have b...</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">Spelling correction has attracted a lot of attention in the NLP community. However, models have been usually evaluated on artificially-created or proprietary corpora. A publicly-available corpus of authentic misspellings, annotated in context, is still lacking. To address this, we present and release an annotated data set of 6,121 spelling errors in context, based on a corpus of essays written by English language learners. We also develop a minimally-supervised context-aware approach to spelling correction. It achieves strong results on our data: 88.12% accuracy. This approach can also train with a minimal amount of annotated data (performance reduced by less than 1%). Furthermore, this approach allows easy porta-bility to new domains. We evaluate our model on data from a medical domain and demonstrate that it rivals the performance of a model trained and tuned on in-domain data.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="eb854d7283bbe5731f08b8cc52c5755c" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":60355773,"asset_id":40139377,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/60355773/download_file?st=MTczMjQ5NDE4MCw4LjIyMi4yMDguMTQ2&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="40139377"><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="40139377"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 40139377; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=40139377]").text(description); $(".js-view-count[data-work-id=40139377]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 40139377; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='40139377']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 40139377, 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: "eb854d7283bbe5731f08b8cc52c5755c" } } $('.js-work-strip[data-work-id=40139377]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":40139377,"title":"A Benchmark Corpus of English Misspellings and a Minimally-supervised Model for Spelling Correction","translated_title":"","metadata":{"abstract":"Spelling correction has attracted a lot of attention in the NLP community. However, models have been usually evaluated on artificially-created or proprietary corpora. A publicly-available corpus of authentic misspellings, annotated in context, is still lacking. To address this, we present and release an annotated data set of 6,121 spelling errors in context, based on a corpus of essays written by English language learners. We also develop a minimally-supervised context-aware approach to spelling correction. It achieves strong results on our data: 88.12% accuracy. This approach can also train with a minimal amount of annotated data (performance reduced by less than 1%). Furthermore, this approach allows easy porta-bility to new domains. 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class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/40138988/How_to_account_for_mispellings_Quantifying_the_benefit_of_character_representations_in_neural_content_scoring_models">How to account for mispellings: Quantifying the benefit of character representations in neural content scoring models</a></div><div class="wp-workCard_item"><span>Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Character-based representations in neural models have been claimed to be a tool to overcome spell...</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">Character-based representations in neural models have been claimed to be a tool to overcome spelling variation in word token-based input. We examine this claim in neural models for content scoring. We formulate precise hypotheses about the possible effects of adding character representations to word-based models and test these hypotheses on large-scale real-world content scoring datasets. We find that, while character representations may provide small performance gains in general, their effectiveness in accounting for spelling variation may be limited. We show that spelling correction can provide larger gains than character representations, and that spelling correction improves the performance of models with character representations. With these insights, we report a new state of the art on the ASAP-SAS short content scoring dataset.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="50c3a54849b997570e94be65a9310e99" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":60355344,"asset_id":40138988,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/60355344/download_file?st=MTczMjQ5NDE4MCw4LjIyMi4yMDguMTQ2&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="40138988"><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="40138988"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 40138988; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=40138988]").text(description); $(".js-view-count[data-work-id=40138988]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 40138988; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='40138988']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 40138988, 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: "50c3a54849b997570e94be65a9310e99" } } $('.js-work-strip[data-work-id=40138988]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":40138988,"title":"How to account for mispellings: Quantifying the benefit of character representations in neural content scoring models","translated_title":"","metadata":{"abstract":"Character-based representations in neural models have been claimed to be a tool to overcome spelling variation in word token-based input. We examine this claim in neural models for content scoring. We formulate precise hypotheses about the possible effects of adding character representations to word-based models and test these hypotheses on large-scale real-world content scoring datasets. We find that, while character representations may provide small performance gains in general, their effectiveness in accounting for spelling variation may be limited. We show that spelling correction can provide larger gains than character representations, and that spelling correction improves the performance of models with character representations. With these insights, we report a new state of the art on the ASAP-SAS short content scoring dataset.","publication_date":{"day":null,"month":null,"year":2019,"errors":{}},"publication_name":"Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications"},"translated_abstract":"Character-based representations in neural models have been claimed to be a tool to overcome spelling variation in word token-based input. We examine this claim in neural models for content scoring. We formulate precise hypotheses about the possible effects of adding character representations to word-based models and test these hypotheses on large-scale real-world content scoring datasets. We find that, while character representations may provide small performance gains in general, their effectiveness in accounting for spelling variation may be limited. We show that spelling correction can provide larger gains than character representations, and that spelling correction improves the performance of models with character representations. 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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="582920" id="papers"><div class="js-work-strip profile--work_container" data-work-id="120067089"><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/120067089/Three_Studies_on_Predicting_Word_Concreteness_with_Embedding_Vectors"><img alt="Research paper thumbnail of Three Studies on Predicting Word Concreteness with Embedding Vectors" class="work-thumbnail" src="https://attachments.academia-assets.com/115337290/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/120067089/Three_Studies_on_Predicting_Word_Concreteness_with_Embedding_Vectors">Three Studies on Predicting Word Concreteness with Embedding Vectors</a></div><div class="wp-workCard_item"><span>The Workshop on Cognitive Aspects of the Lexicon </span><span>, 2024</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Human-assigned concreteness ratings for words are commonly used in psycholinguistic and computati...</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">Human-assigned concreteness ratings for words are commonly used in psycholinguistic and computational linguistic studies. Previous research has shown that such ratings can be modeled and extrapolated by using dense word-embedding representations. However, due to rater disagreement, considerable amounts of human ratings in published datasets are not reliable. We investigate how such unreliable data influences modeling of concreteness with word embeddings. Study 1 compares fourteen embedding models over three datasets of concreteness ratings, showing that most models achieve high correlations with human ratings, and exhibit low error rates on predictions. Study 2 investigates how exclusion of the less reliable ratings influences the modeling results. It indicates that improved results can be achieved when data is cleaned. Study 3 adds additional conditions over those of study 2 and indicates that the improved results hold only for the cleaned data, and that in the general case removing the less reliable data points is not useful.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="ae22b309ae55e0be68210490bc369c0a" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":115337290,"asset_id":120067089,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/115337290/download_file?st=MTczMjQ5NDE4MCw4LjIyMi4yMDguMTQ2&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="120067089"><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="120067089"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 120067089; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=120067089]").text(description); $(".js-view-count[data-work-id=120067089]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 120067089; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='120067089']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 120067089, 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: "ae22b309ae55e0be68210490bc369c0a" } } $('.js-work-strip[data-work-id=120067089]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":120067089,"title":"Three Studies on Predicting Word Concreteness with Embedding Vectors","translated_title":"","metadata":{"abstract":"Human-assigned concreteness ratings for words are commonly used in psycholinguistic and computational linguistic studies. Previous research has shown that such ratings can be modeled and extrapolated by using dense word-embedding representations. However, due to rater disagreement, considerable amounts of human ratings in published datasets are not reliable. We investigate how such unreliable data influences modeling of concreteness with word embeddings. Study 1 compares fourteen embedding models over three datasets of concreteness ratings, showing that most models achieve high correlations with human ratings, and exhibit low error rates on predictions. Study 2 investigates how exclusion of the less reliable ratings influences the modeling results. It indicates that improved results can be achieved when data is cleaned. Study 3 adds additional conditions over those of study 2 and indicates that the improved results hold only for the cleaned data, and that in the general case removing the less reliable data points is not useful.","publication_date":{"day":null,"month":null,"year":2024,"errors":{}},"publication_name":"The Workshop on Cognitive Aspects of the Lexicon "},"translated_abstract":"Human-assigned concreteness ratings for words are commonly used in psycholinguistic and computational linguistic studies. Previous research has shown that such ratings can be modeled and extrapolated by using dense word-embedding representations. However, due to rater disagreement, considerable amounts of human ratings in published datasets are not reliable. We investigate how such unreliable data influences modeling of concreteness with word embeddings. Study 1 compares fourteen embedding models over three datasets of concreteness ratings, showing that most models achieve high correlations with human ratings, and exhibit low error rates on predictions. Study 2 investigates how exclusion of the less reliable ratings influences the modeling results. It indicates that improved results can be achieved when data is cleaned. Study 3 adds additional conditions over those of study 2 and indicates that the improved results hold only for the cleaned data, and that in the general case removing the less reliable data points is not useful.","internal_url":"https://www.academia.edu/120067089/Three_Studies_on_Predicting_Word_Concreteness_with_Embedding_Vectors","translated_internal_url":"","created_at":"2024-05-26T16:31:30.901-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":3199073,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":41758098,"work_id":120067089,"tagging_user_id":3199073,"tagged_user_id":null,"co_author_invite_id":7079990,"email":"m***r@ets.org","display_order":1,"name":"Michael Flor","title":"Three Studies on Predicting Word Concreteness with Embedding Vectors"}],"downloadable_attachments":[{"id":115337290,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115337290/thumbnails/1.jpg","file_name":"Three_Studies_on_Predicting_Word_Concreteness_with_Embedding_Vectors_2024_.pdf","download_url":"https://www.academia.edu/attachments/115337290/download_file?st=MTczMjQ5NDE4MCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Three_Studies_on_Predicting_Word_Concret.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115337290/Three_Studies_on_Predicting_Word_Concreteness_with_Embedding_Vectors_2024_-libre.pdf?1716768810=\u0026response-content-disposition=attachment%3B+filename%3DThree_Studies_on_Predicting_Word_Concret.pdf\u0026Expires=1732474250\u0026Signature=Ix-Ry2T1FUQE~TwJxTx-evgYHeCzC6vF-jHag31Jx9KmjtwQPmWKymHk5KRWdpuRh9QbNtJw8ZzRdXNDHVPfYgz-nF2VLCDRY-cnCOfEsRxX5zNo-Tz0mHJU989H39DqbOOaDgKp7Et4DSYSIoIFOJDS~7AXYKhuULJUyatsU6-f57yRuR8HzupFQ4AkEJdPIQfPjPdZ66v-gkIqz0dMJ-1jIxlgLUwO2aCBCZ6zu1O2K~eYd6czrzhPJk7cYcKK4xnf-19f3NhXy0hN5OozwJ6XKLpJuC4hGpVH043KjjoycNNW~sh6hsvp9lbqelBDb~lbbQDUYHjya56Gj~l8tA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Three_Studies_on_Predicting_Word_Concreteness_with_Embedding_Vectors","translated_slug":"","page_count":11,"language":"en","content_type":"Work","owner":{"id":3199073,"first_name":"Michael","middle_initials":null,"last_name":"Flor","page_name":"MichaelFlor","domain_name":"ets","created_at":"2013-01-24T00:10:26.300-08:00","display_name":"Michael Flor","url":"https://ets.academia.edu/MichaelFlor","email":"WXBRUXdhakhxRXhEQmdrSU9hUjE0clBzeVp6N3ZjVHBPQy9KbEwwTTFJRzc0OHhUQ1RWMHE5azE3U0FhRGZVZy0tSXdkTVRPeHlMS1dYbFVSQkV3UWhDdz09--225956895ffd411dd80db3e1355c4c8bd3b4548c"},"attachments":[{"id":115337290,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/115337290/thumbnails/1.jpg","file_name":"Three_Studies_on_Predicting_Word_Concreteness_with_Embedding_Vectors_2024_.pdf","download_url":"https://www.academia.edu/attachments/115337290/download_file?st=MTczMjQ5NDE4MCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Three_Studies_on_Predicting_Word_Concret.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/115337290/Three_Studies_on_Predicting_Word_Concreteness_with_Embedding_Vectors_2024_-libre.pdf?1716768810=\u0026response-content-disposition=attachment%3B+filename%3DThree_Studies_on_Predicting_Word_Concret.pdf\u0026Expires=1732474250\u0026Signature=Ix-Ry2T1FUQE~TwJxTx-evgYHeCzC6vF-jHag31Jx9KmjtwQPmWKymHk5KRWdpuRh9QbNtJw8ZzRdXNDHVPfYgz-nF2VLCDRY-cnCOfEsRxX5zNo-Tz0mHJU989H39DqbOOaDgKp7Et4DSYSIoIFOJDS~7AXYKhuULJUyatsU6-f57yRuR8HzupFQ4AkEJdPIQfPjPdZ66v-gkIqz0dMJ-1jIxlgLUwO2aCBCZ6zu1O2K~eYd6czrzhPJk7cYcKK4xnf-19f3NhXy0hN5OozwJ6XKLpJuC4hGpVH043KjjoycNNW~sh6hsvp9lbqelBDb~lbbQDUYHjya56Gj~l8tA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":30608,"name":"Cognitive Lexical Semantics","url":"https://www.academia.edu/Documents/in/Cognitive_Lexical_Semantics"},{"id":98490,"name":"Mental Lexicon","url":"https://www.academia.edu/Documents/in/Mental_Lexicon"},{"id":332649,"name":"Concreteness","url":"https://www.academia.edu/Documents/in/Concreteness"},{"id":1819738,"name":"Word Embedding","url":"https://www.academia.edu/Documents/in/Word_Embedding"}],"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="116602324"><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/116602324/Mapping_of_American_English_vocabulary_by_grade_levels"><img alt="Research paper thumbnail of Mapping of American English vocabulary by grade levels" class="work-thumbnail" src="https://attachments.academia-assets.com/112686421/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/116602324/Mapping_of_American_English_vocabulary_by_grade_levels">Mapping of American English vocabulary by grade levels</a></div><div class="wp-workCard_item"><span> ITL - International Journal of Applied Linguistics </span><span>, 2024</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We describe a large-scale effort to map English-language vocabulary by U.S. school grade levels. ...</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">We describe a large-scale effort to map English-language vocabulary by U.S. school grade levels. Our motivation is to rapidly expand graded vocabulary resources for work with native English speakers in the USA, while taking into consideration school-related influences rather than relying on just the corpus-frequency approaches. We report on the initial effort of data collection, with mapping of about 22K word forms. We provide comparisons of this mapping to some other recent vocabulary mapping efforts, such as age-of-acquisition. We then describe the efforts to automatically expand this resource by using linguistically motivated variables and corpus-based methods. Our current resource maps more than 126K English word forms to US school grade levels. We also compare a subset of our L1 mapped data to English L2 vocabulary levels, as expressed on the CEFR scale, and find that there is a considerable overlap in the order of vocabulary learning in L1 and</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="b5fb58bf5425f07079e39fbdd28fc212" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":112686421,"asset_id":116602324,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/112686421/download_file?st=MTczMjQ5NDE4MCw4LjIyMi4yMDguMTQ2&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="116602324"><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="116602324"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 116602324; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=116602324]").text(description); $(".js-view-count[data-work-id=116602324]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 116602324; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='116602324']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 116602324, 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: "b5fb58bf5425f07079e39fbdd28fc212" } } $('.js-work-strip[data-work-id=116602324]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":116602324,"title":"Mapping of American English vocabulary by grade levels","translated_title":"","metadata":{"doi":"10.1075/itl.22025.flo","abstract":"We describe a large-scale effort to map English-language vocabulary by U.S. school grade levels. 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We also compare a subset of our L1 mapped data to English L2 vocabulary levels, as expressed on the CEFR scale, and find that there is a considerable overlap in the order of vocabulary learning in L1 and","publication_date":{"day":null,"month":null,"year":2024,"errors":{}},"publication_name":" ITL - International Journal of Applied Linguistics "},"translated_abstract":"We describe a large-scale effort to map English-language vocabulary by U.S. school grade levels. Our motivation is to rapidly expand graded vocabulary resources for work with native English speakers in the USA, while taking into consideration school-related influences rather than relying on just the corpus-frequency approaches. We report on the initial effort of data collection, with mapping of about 22K word forms. We provide comparisons of this mapping to some other recent vocabulary mapping efforts, such as age-of-acquisition. 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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="82813749"><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/82813749/Exploring_Automated_Classification_Approaches_to_Advance_the_Assessment_of_Collaborative_Problem_Solving_Skills"><img alt="Research paper thumbnail of Exploring Automated Classification Approaches to Advance the Assessment of Collaborative Problem Solving Skills" 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/82813749/Exploring_Automated_Classification_Approaches_to_Advance_the_Assessment_of_Collaborative_Problem_Solving_Skills">Exploring Automated Classification Approaches to Advance the Assessment of Collaborative Problem Solving Skills</a></div><div class="wp-workCard_item"><span>Journal of Intelligence</span><span>, Jul 4, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Competency in skills associated with collaborative problem solving (CPS) is critical for many con...</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">Competency in skills associated with collaborative problem solving (CPS) is critical for many contexts, including school, the workplace, and the military. Innovative approaches for assessing individuals’ CPS competency are necessary, as traditional assessment types such as multiple -choice items are not well suited for such a process-oriented competency. In a move to computer-based environments to support CPS assessment, innovative computational approaches are also needed to understand individuals’ CPS behaviors. In the current study, we describe the use of a simulation-based task on electronics concepts as an environment for higher education students to display evidence of their CPS competency. We further describe computational linguistic methods for automatically characterizing students’ display of various CPS skills in the task. Comparisons between such an automated approach and an approach based on human annotation to characterize student CPS behaviors revealed above average agreement. These results give credence to the potential for automated approaches to help advance the assessment of CPS and to circumvent the time-intensive human annotation approaches that are typically used in these contexts.</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="82813749"><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="82813749"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 82813749; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=82813749]").text(description); $(".js-view-count[data-work-id=82813749]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 82813749; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='82813749']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 82813749, 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" } } $('.js-work-strip[data-work-id=82813749]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":82813749,"title":"Exploring Automated Classification Approaches to Advance the Assessment of Collaborative Problem Solving Skills","translated_title":"","metadata":{"doi":"10.3390/jintelligence10030039","abstract":"Competency in skills associated with collaborative problem solving (CPS) is critical for many contexts, including school, the workplace, and the military. Innovative approaches for assessing individuals’ CPS competency are necessary, as traditional assessment types such as multiple -choice items are not well suited for such a process-oriented competency. In a move to computer-based environments to support CPS assessment, innovative computational approaches are also needed to understand individuals’ CPS behaviors. In the current study, we describe the use of a simulation-based task on electronics concepts as an environment for higher education students to display evidence of their CPS competency. We further describe computational linguistic methods for automatically characterizing students’ display of various CPS skills in the task. Comparisons between such an automated approach and an approach based on human annotation to characterize student CPS behaviors revealed above average agreement. These results give credence to the potential for automated approaches to help advance the assessment of CPS and to circumvent the time-intensive human annotation approaches that are typically used in these contexts.","publication_date":{"day":4,"month":7,"year":2022,"errors":{}},"publication_name":"Journal of Intelligence"},"translated_abstract":"Competency in skills associated with collaborative problem solving (CPS) is critical for many contexts, including school, the workplace, and the military. Innovative approaches for assessing individuals’ CPS competency are necessary, as traditional assessment types such as multiple -choice items are not well suited for such a process-oriented competency. In a move to computer-based environments to support CPS assessment, innovative computational approaches are also needed to understand individuals’ CPS behaviors. In the current study, we describe the use of a simulation-based task on electronics concepts as an environment for higher education students to display evidence of their CPS competency. We further describe computational linguistic methods for automatically characterizing students’ display of various CPS skills in the task. Comparisons between such an automated approach and an approach based on human annotation to characterize student CPS behaviors revealed above average agreement. These results give credence to the potential for automated approaches to help advance the assessment of CPS and to circumvent the time-intensive human annotation approaches that are typically used in these contexts.","internal_url":"https://www.academia.edu/82813749/Exploring_Automated_Classification_Approaches_to_Advance_the_Assessment_of_Collaborative_Problem_Solving_Skills","translated_internal_url":"","created_at":"2022-07-08T12:59:41.987-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":3199073,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Exploring_Automated_Classification_Approaches_to_Advance_the_Assessment_of_Collaborative_Problem_Solving_Skills","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":3199073,"first_name":"Michael","middle_initials":null,"last_name":"Flor","page_name":"MichaelFlor","domain_name":"ets","created_at":"2013-01-24T00:10:26.300-08:00","display_name":"Michael Flor","url":"https://ets.academia.edu/MichaelFlor"},"attachments":[],"research_interests":[{"id":94,"name":"Discourse Analysis","url":"https://www.academia.edu/Documents/in/Discourse_Analysis"},{"id":4828,"name":"Collaboration","url":"https://www.academia.edu/Documents/in/Collaboration"},{"id":8679,"name":"Computer Supported Collaborative Learning (CSCL)","url":"https://www.academia.edu/Documents/in/Computer_Supported_Collaborative_Learning_CSCL_"},{"id":3521305,"name":"Automated Text Classification","url":"https://www.academia.edu/Documents/in/Automated_Text_Classification"}],"urls":[{"id":22017757,"url":"https://www.mdpi.com/2079-3200/10/3/39"}]}, 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="79783863"><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/79783863/Towards_automatic_annotation_of_collaborative_problem_solving_skills_in_technology_enhanced_environments"><img alt="Research paper thumbnail of Towards automatic annotation of collaborative problem-solving skills in technology-enhanced environments" class="work-thumbnail" src="https://attachments.academia-assets.com/94847882/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/79783863/Towards_automatic_annotation_of_collaborative_problem_solving_skills_in_technology_enhanced_environments">Towards automatic annotation of collaborative problem-solving skills in technology-enhanced environments</a></div><div class="wp-workCard_item"><span>Journal of Computer Assisted Learning</span><span>, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Objective: We explore possibilities for automated annotation of actions in collaborative-teams, ...</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">Objective: We explore possibilities for automated annotation of actions in <br />collaborative-teams, especially chat messages. We evaluate two approaches that <br />employ machine learning for automated classification of CPS events. <br />Method: Data were collected from engineering, physics and electronics students' <br />participation in a simulation-based task on electronics concepts, in which participants <br />communicated via text-chat messages. All task activities were logged and time <br />stamped. Data have been manually classified for the CPS skills, using an ontology <br />that includes both social and cognitive dimensions. In this article, we describe computational linguistic methods for automatically classifying the CPS skills from logged data, with a view towards automating CPS assessments. <br />Results: We applied two machine learning methods to our data. A Naïve Bayes classifier has been previously used in CPS research, but it is only moderately successful on our data. We also present a k-nearest-neighbours (kNN) classifier that uses distributional semantic models for measuring text similarity. This classifier shows strong agreement between automated and human annotations. The study also demonstrates that automatic spelling correction and slang normalization of chat texts are useful for accurate automated annotation.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="abc72262b35cbc2ace3413a8845369fe" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":94847882,"asset_id":79783863,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/94847882/download_file?st=MTczMjQ5NDE4MCw4LjIyMi4yMDguMTQ2&st=MTczMjQ5NDE3OSw4LjIyMi4yMDguMTQ2&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="79783863"><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="79783863"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79783863; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=79783863]").text(description); $(".js-view-count[data-work-id=79783863]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 79783863; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='79783863']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 79783863, 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: "abc72262b35cbc2ace3413a8845369fe" } } $('.js-work-strip[data-work-id=79783863]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":79783863,"title":"Towards automatic annotation of collaborative problem-solving skills in technology-enhanced environments","translated_title":"","metadata":{"doi":"10.1111/jcal.12689","abstract":"Objective: We explore possibilities for automated annotation of actions in\r\ncollaborative-teams, especially chat messages. We evaluate two approaches that\r\nemploy machine learning for automated classification of CPS events.\r\nMethod: Data were collected from engineering, physics and electronics students'\r\nparticipation in a simulation-based task on electronics concepts, in which participants\r\ncommunicated via text-chat messages. All task activities were logged and time\r\nstamped. Data have been manually classified for the CPS skills, using an ontology\r\nthat includes both social and cognitive dimensions. In this article, we describe computational linguistic methods for automatically classifying the CPS skills from logged data, with a view towards automating CPS assessments.\r\nResults: We applied two machine learning methods to our data. A Naïve Bayes classifier has been previously used in CPS research, but it is only moderately successful on our data. We also present a k-nearest-neighbours (kNN) classifier that uses distributional semantic models for measuring text similarity. This classifier shows strong agreement between automated and human annotations. The study also demonstrates that automatic spelling correction and slang normalization of chat texts are useful for accurate automated annotation.","publication_date":{"day":null,"month":null,"year":2022,"errors":{}},"publication_name":"Journal of Computer Assisted Learning"},"translated_abstract":"Objective: We explore possibilities for automated annotation of actions in\r\ncollaborative-teams, especially chat messages. We evaluate two approaches that\r\nemploy machine learning for automated classification of CPS events.\r\nMethod: Data were collected from engineering, physics and electronics students'\r\nparticipation in a simulation-based task on electronics concepts, in which participants\r\ncommunicated via text-chat messages. All task activities were logged and time\r\nstamped. Data have been manually classified for the CPS skills, using an ontology\r\nthat includes both social and cognitive dimensions. In this article, we describe computational linguistic methods for automatically classifying the CPS skills from logged data, with a view towards automating CPS assessments.\r\nResults: We applied two machine learning methods to our data. A Naïve Bayes classifier has been previously used in CPS research, but it is only moderately successful on our data. We also present a k-nearest-neighbours (kNN) classifier that uses distributional semantic models for measuring text similarity. This classifier shows strong agreement between automated and human annotations. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="77225151"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/77225151/Text_Mining_and_Automated_Scoring"><img alt="Research paper thumbnail of Text Mining and Automated Scoring" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/77225151/Text_Mining_and_Automated_Scoring">Text Mining and Automated Scoring</a></div><div class="wp-workCard_item"><span>Methodology of Educational Measurement and Assessment</span><span>, 2021</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="77225151"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="77225151"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 77225151; 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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="71504367"><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/71504367/A_Corpus_of_Non_Native_Written_English_Annotated_for_Metaphor"><img alt="Research paper thumbnail of A Corpus of Non-Native Written English Annotated for Metaphor" class="work-thumbnail" src="https://attachments.academia-assets.com/80818253/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/71504367/A_Corpus_of_Non_Native_Written_English_Annotated_for_Metaphor">A Corpus of Non-Native Written English Annotated for Metaphor</a></div><div class="wp-workCard_item"><span>Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)</span><span>, 2018</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="acd9687502a1d10c87ee6c803e694cd2" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":80818253,"asset_id":71504367,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/80818253/download_file?st=MTczMjQ5NDE4MCw4LjIyMi4yMDguMTQ2&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="71504367"><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="71504367"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 71504367; 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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="65530897"><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/65530897/Associative_Texture_Is_Lost_In_Translation"><img alt="Research paper thumbnail of Associative Texture Is Lost In Translation" class="work-thumbnail" src="https://attachments.academia-assets.com/77085349/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/65530897/Associative_Texture_Is_Lost_In_Translation">Associative Texture Is Lost In Translation</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We present a suggestive finding regarding the loss of associative texture in the process of machi...</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">We present a suggestive finding regarding the loss of associative texture in the process of machine translation, using comparisons between (a) original and backtranslated texts, (b) reference and system translations, and (c) better and worse MT systems. We represent the amount of association in a text using word association profile ‐ a distribution of pointwise mutual information between all pairs of content word types in a text. We use the average of the distribution, which we term lexical tightness, as a single measure of the amount of association in a text. We show that the lexical tightness of humancomposed texts is higher than that of the machine translated materials; human references are tighter than machine translations, and better MT systems produce lexically tighter translations. While the phenomenon of the loss of associative texture has been theoretically predicted by translation scholars, we present a measure capable of quantifying the extent of this phenomenon.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f661940b2fce063835e98c9cba9ba38b" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":77085349,"asset_id":65530897,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/77085349/download_file?st=MTczMjQ5NDE4MCw4LjIyMi4yMDguMTQ2&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="65530897"><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="65530897"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 65530897; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=65530897]").text(description); $(".js-view-count[data-work-id=65530897]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 65530897; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='65530897']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 65530897, 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: "f661940b2fce063835e98c9cba9ba38b" } } $('.js-work-strip[data-work-id=65530897]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":65530897,"title":"Associative Texture Is Lost In Translation","translated_title":"","metadata":{"abstract":"We present a suggestive finding regarding the loss of associative texture in the process of machine translation, using comparisons between (a) original and backtranslated texts, (b) reference and system translations, and (c) better and worse MT systems. 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class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/65530896/On_using_context_for_automatic_correction_of_non_word_misspellings_in_student_essays"><img alt="Research paper thumbnail of On using context for automatic correction of non-word misspellings in student essays" class="work-thumbnail" src="https://attachments.academia-assets.com/77085580/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/65530896/On_using_context_for_automatic_correction_of_non_word_misspellings_in_student_essays">On using context for automatic correction of non-word misspellings in student essays</a></div><div class="wp-workCard_item"><span>The 7th Workshop on Innovative Use of NLP for Building Educational Applications, 2012</span><span>, 2012</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In this paper we present a new spell-checking system that utilizes contextual information for aut...</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 this paper we present a new spell-checking system that utilizes contextual information for automatic correction of non-word misspellings. The system is evaluated with a large corpus of essays written by native and non-native speakers of English to the writing prompts of high-stakes standardized tests (TOEFL® and GRE®). We also present comparative evaluations with Aspell and the speller from Microsoft Office 2007. Using context-informed re-ranking of candidate suggestions, our system exhibits superior error-correction results overall and also corrects errors generated by non-native English writers with almost same rate of success as it does for writers who are native English speakers.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="1830e459a32c91be0b147b6515ba47fa" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":77085580,"asset_id":65530896,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/77085580/download_file?st=MTczMjQ5NDE4MCw4LjIyMi4yMDguMTQ2&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="65530896"><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="65530896"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 65530896; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=65530896]").text(description); $(".js-view-count[data-work-id=65530896]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 65530896; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='65530896']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 65530896, 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: "1830e459a32c91be0b147b6515ba47fa" } } $('.js-work-strip[data-work-id=65530896]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":65530896,"title":"On using context for automatic correction of non-word misspellings in student essays","translated_title":"","metadata":{"abstract":"In this paper we present a new spell-checking system that utilizes contextual information for automatic correction of non-word misspellings. 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with automatic spelling correction" 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/65530895/Producing_an_annotated_corpus_with_automatic_spelling_correction">Producing an annotated corpus with automatic spelling correction</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper describes ConSpel, a software system for automatic detection and correction of non-wor...</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 describes ConSpel, a software system for automatic detection and correction of non-word misspellings. We also present an ongoing research project for constructing an ETS (Educational Testing Service) Spelling Corpus. The corpus consists of essays written by native and non-native speakers of English to the writing prompts of TOEFL® and GRE® tests. Essays are annotated for misspellings by trained annotators, using a semi-automated methodology. An evaluation of the ConSpel system was conducted, using the data from the completed phase of the annotation project. The ConSpel system achieves above 95% accuracy in error detection. 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The quality of argumentation being the focus of the project, we developed a metaphor annotation protocol that targets metaphors that are relevant for the writer’s arguments. The reliability of the protocol is =0.58, on a set of 116 essays (the total of about 30K content-word tokens). We found a moderate-to-strong correlation (r=0.51-0.57) between the percentage of metaphorically used words in an essay and the writing quality score. 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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="65530753"><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/65530753/Developing_an_e_rater_Advisory_to_detect_Babel_generated_essays"><img alt="Research paper thumbnail of Developing an e-rater Advisory to detect Babel-generated essays" class="work-thumbnail" src="https://attachments.academia-assets.com/77085300/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/65530753/Developing_an_e_rater_Advisory_to_detect_Babel_generated_essays">Developing an e-rater Advisory to detect Babel-generated essays</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Background: It is important for developers of automated scoring systems to ensure that their syst...</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">Background: It is important for developers of automated scoring systems to ensure that their systems are as fair and valid as possible. This commitment means evaluating the performance of these systems in light of construct-irrelevant response strategies. The enhancement of systems to detect and deal with these kinds of strategies is often an iterative process, whereby as new strategies come to light they need to be evaluated and effective mechanisms built into the automated scoring systems to handle them. In this paper, we focus on the Babel system, which automatically generates semantically incohesive essays. We expect that these essays may unfairly receive high scores from automated scoring engines despite essentially being nonsense. Literature Review: We discuss literature related to gaming of automated scoring systems. One reason that Babel essays are so easy to identify as nonsense by human readers is that they lack any semantic cohesion. Therefore, we also discuss some litera...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="9ab3c10cc0e4987c2e0e28804c68eca6" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":77085300,"asset_id":65530753,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/77085300/download_file?st=MTczMjQ5NDE4MCw4LjIyMi4yMDguMTQ2&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="65530753"><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="65530753"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 65530753; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=65530753]").text(description); $(".js-view-count[data-work-id=65530753]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 65530753; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='65530753']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 65530753, 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: "9ab3c10cc0e4987c2e0e28804c68eca6" } } $('.js-work-strip[data-work-id=65530753]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":65530753,"title":"Developing an e-rater Advisory to detect Babel-generated essays","translated_title":"","metadata":{"abstract":"Background: It is important for developers of automated scoring systems to ensure that their systems are as fair and valid as possible. This commitment means evaluating the performance of these systems in light of construct-irrelevant response strategies. The enhancement of systems to detect and deal with these kinds of strategies is often an iterative process, whereby as new strategies come to light they need to be evaluated and effective mechanisms built into the automated scoring systems to handle them. In this paper, we focus on the Babel system, which automatically generates semantically incohesive essays. We expect that these essays may unfairly receive high scores from automated scoring engines despite essentially being nonsense. Literature Review: We discuss literature related to gaming of automated scoring systems. One reason that Babel essays are so easy to identify as nonsense by human readers is that they lack any semantic cohesion. Therefore, we also discuss some litera...","publication_date":{"day":null,"month":null,"year":2018,"errors":{}}},"translated_abstract":"Background: It is important for developers of automated scoring systems to ensure that their systems are as fair and valid as possible. This commitment means evaluating the performance of these systems in light of construct-irrelevant response strategies. The enhancement of systems to detect and deal with these kinds of strategies is often an iterative process, whereby as new strategies come to light they need to be evaluated and effective mechanisms built into the automated scoring systems to handle them. In this paper, we focus on the Babel system, which automatically generates semantically incohesive essays. We expect that these essays may unfairly receive high scores from automated scoring engines despite essentially being nonsense. Literature Review: We discuss literature related to gaming of automated scoring systems. One reason that Babel essays are so easy to identify as nonsense by human readers is that they lack any semantic cohesion. 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analytics","url":"https://www.academia.edu/Documents/in/writing_analytics"}],"urls":[{"id":15542019,"url":"https://wac.colostate.edu/docs/jwa/vol2/cahill.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="49252725"><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/49252725/Systems_and_methods_for_automatic_generation_of_questions_from_text"><img alt="Research paper thumbnail of Systems and methods for automatic generation of questions from text." class="work-thumbnail" src="https://attachments.academia-assets.com/67636432/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/49252725/Systems_and_methods_for_automatic_generation_of_questions_from_text">Systems and methods for automatic generation of questions from text.</a></div><div class="wp-workCard_item"><span>USPTO</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Computer-implemented systems and methods are described herein for automatically generating questi...</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">Computer-implemented systems and methods are described herein for automatically generating questions from text. Text including one or more sentences is received. A sentence, comprising a predicate and one or more arguments associated with the predicate, is parsed from the text. Semantic role labels are assigned to the one or more arguments associated with the predicate. One or more questions are automatically generated relating to the predicate based on the assigned semantic role labels. Each answer to the generated questions is one of the one or more arguments associated with the predicate.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="ad66c6a314c001f8e2ac630d28b9a97d" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":67636432,"asset_id":49252725,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/67636432/download_file?st=MTczMjQ5NDE4MCw4LjIyMi4yMDguMTQ2&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="49252725"><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="49252725"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 49252725; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=49252725]").text(description); $(".js-view-count[data-work-id=49252725]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 49252725; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='49252725']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 49252725, 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: "ad66c6a314c001f8e2ac630d28b9a97d" } } $('.js-work-strip[data-work-id=49252725]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":49252725,"title":"Systems and methods for automatic generation of questions from text.","translated_title":"","metadata":{"abstract":"Computer-implemented systems and methods are described herein for automatically generating questions from text. 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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="43733778"><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/43733778/Emotion_Arcs_of_Student_Narratives"><img alt="Research paper thumbnail of Emotion Arcs of Student Narratives" class="work-thumbnail" src="https://attachments.academia-assets.com/64043676/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/43733778/Emotion_Arcs_of_Student_Narratives">Emotion Arcs of Student Narratives</a></div><div class="wp-workCard_item"><span>Proceedings of the First Joint Workshop on Narrative Understanding, Storylines, and Events</span><span>, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper studies emotion arcs in student narratives. We construct emotion arcs based on event 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">This paper studies emotion arcs in student narratives. We construct emotion arcs based on event affect and implied sentiments, which correspond to plot elements in the story. We show that student narratives can show elements of plot structure in their emotion arcs and that properties of these arcs can be useful indicators of narrative quality. We build a system and perform analysis to show that our arc-based features are complementary to previously studied sentiment features in this area.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="41f57ec42a6f85ed9c990ac01ee0053c" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":64043676,"asset_id":43733778,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/64043676/download_file?st=MTczMjQ5NDE4MCw4LjIyMi4yMDguMTQ2&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="43733778"><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="43733778"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 43733778; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=43733778]").text(description); $(".js-view-count[data-work-id=43733778]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 43733778; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='43733778']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 43733778, 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: "41f57ec42a6f85ed9c990ac01ee0053c" } } $('.js-work-strip[data-work-id=43733778]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":43733778,"title":"Emotion Arcs of Student Narratives","translated_title":"","metadata":{"abstract":"This paper studies emotion arcs in student narratives. 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href="https://www.academia.edu/43733711/Go_Figure_Multi_task_transformer_based_architecture_for_metaphor_detection_using_idioms_ETS_team_in_2020_metaphor_shared_task"><img alt="Research paper thumbnail of Go Figure! Multi-task transformer-based architecture for metaphor detection using idioms: ETS team in 2020 metaphor shared task" class="work-thumbnail" src="https://attachments.academia-assets.com/64043619/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/43733711/Go_Figure_Multi_task_transformer_based_architecture_for_metaphor_detection_using_idioms_ETS_team_in_2020_metaphor_shared_task">Go Figure! Multi-task transformer-based architecture for metaphor detection using idioms: ETS team in 2020 metaphor shared task</a></div><div class="wp-workCard_item"><span>Proceedings of the Second Workshop on Figurative Language Processing</span><span>, Jul 9, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper describes the ETS entry to the 2020 Metaphor Detection shared task. Our contribution c...</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 describes the ETS entry to the 2020 Metaphor Detection shared task. Our contribution consists of a sequence of experiments using BERT, starting with a baseline, strengthening it by spell-correcting the TOEFL corpus , followed by a multi-task learning setting , where one of the tasks is the token-level metaphor classification as per the shared task, while the other is meant to provide additional training that we hypothesized to be relevant to the main task. In one case, out-of-domain data manually annotated for metaphor is used for the auxiliary task; in the other case, in-domain data automatically annotated for idioms is used for the auxiliary task. Both multi-task experiments yield promising results.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="059128da26fc7f2d5343318f3310b663" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":64043619,"asset_id":43733711,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/64043619/download_file?st=MTczMjQ5NDE4MCw4LjIyMi4yMDguMTQ2&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="43733711"><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="43733711"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 43733711; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=43733711]").text(description); $(".js-view-count[data-work-id=43733711]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 43733711; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='43733711']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 43733711, 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: "059128da26fc7f2d5343318f3310b663" } } $('.js-work-strip[data-work-id=43733711]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":43733711,"title":"Go Figure! 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In one case, out-of-domain data manually annotated for metaphor is used for the auxiliary task; in the other case, in-domain data automatically annotated for idioms is used for the auxiliary task. Both multi-task experiments yield promising results.","internal_url":"https://www.academia.edu/43733711/Go_Figure_Multi_task_transformer_based_architecture_for_metaphor_detection_using_idioms_ETS_team_in_2020_metaphor_shared_task","translated_internal_url":"","created_at":"2020-07-28T19:55:17.649-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":3199073,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":35407010,"work_id":43733711,"tagging_user_id":3199073,"tagged_user_id":null,"co_author_invite_id":7079985,"email":"x***2@ets.org","display_order":-2,"name":"Xianyang Chen","title":"Go Figure! 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We pres...</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 study explores the relation between lexical concreteness and narrative text quality. We present a methodology to quantitatively measure lexical concreteness of a text. We apply it to a corpus of student stories, scored according to writing evaluation rubrics. Lexical concreteness is weakly-to-moderately related to story quality, depending on story-type. The relation is mostly borne by adjectives and nouns, but also found for adverbs and verbs.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="a05a5667b7dbe609efea2ac12a2cf75c" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":60355929,"asset_id":40139495,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/60355929/download_file?st=MTczMjQ5NDE4MCw4LjIyMi4yMDguMTQ2&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="40139495"><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="40139495"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 40139495; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=40139495]").text(description); $(".js-view-count[data-work-id=40139495]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 40139495; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='40139495']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 40139495, 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: "a05a5667b7dbe609efea2ac12a2cf75c" } } $('.js-work-strip[data-work-id=40139495]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":40139495,"title":"Lexical concreteness in narrative","translated_title":"","metadata":{"abstract":"This study explores the relation between lexical concreteness and narrative text quality. 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The relation is mostly borne by adjectives and nouns, but also found for adverbs and verbs.","internal_url":"https://www.academia.edu/40139495/Lexical_concreteness_in_narrative","translated_internal_url":"","created_at":"2019-08-21T09:03:00.151-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":3199073,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":32925400,"work_id":40139495,"tagging_user_id":3199073,"tagged_user_id":null,"co_author_invite_id":6184186,"email":"s***n@ets.org","display_order":2,"name":"Swapna Somasundaran","title":"Lexical concreteness in narrative"}],"downloadable_attachments":[{"id":60355929,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/60355929/thumbnails/1.jpg","file_name":"Lexical_concreteness_in_narrative__2019_20190821-81645-19pboop.pdf","download_url":"https://www.academia.edu/attachments/60355929/download_file?st=MTczMjQ5NDE4MCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Lexical_concreteness_in_narrative.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/60355929/Lexical_concreteness_in_narrative__2019_20190821-81645-19pboop-libre.pdf?1566406235=\u0026response-content-disposition=attachment%3B+filename%3DLexical_concreteness_in_narrative.pdf\u0026Expires=1732474251\u0026Signature=N-PzOZOTgA7KpxomufM6HJ7kd9emvcmIBud71jD0QSPnzr4VV27X96JI1~RtNwn-SOF4awM1QuAhSfRF2dAQHkUaKN-tMNl1wlfmw-bT3xI2KCxvaY6nOvGL~bTCs5PcvWoVeoWH~7vB3xdInu0Gf7gtHE0nHM87W-1rzt8u3ZkG64XDuxWEjc8GSerEvyWRwiJL4moBo2xOehwQ5mkNpShtL7ukqpgz69oOlDZoC7eKXu5ppg~KT5nWOBPq878~aNTl01KL8fJy5B0Ua6~QWAugdwlyu3bINOd-64I3xWQht7EBhICBjJ48P8lpA1HUcolFQKD8gaiwPkg~MK6TjQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Lexical_concreteness_in_narrative","translated_slug":"","page_count":6,"language":"en","content_type":"Work","owner":{"id":3199073,"first_name":"Michael","middle_initials":null,"last_name":"Flor","page_name":"MichaelFlor","domain_name":"ets","created_at":"2013-01-24T00:10:26.300-08:00","display_name":"Michael Flor","url":"https://ets.academia.edu/MichaelFlor","email":"MzNUM0JZUkUwd0ErUzk1UE9BZFdCRXh5TVFmUW9YVzlvdUZBY3Z5VjVPWTM4aFVWY29oeFpXS3BROEhQei9XSy0tMllRM09FWUtYK2FndGFtbHI3UHRLdz09--2b0dea0622fe80808bd25eeafde2dffe19423c6a"},"attachments":[{"id":60355929,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/60355929/thumbnails/1.jpg","file_name":"Lexical_concreteness_in_narrative__2019_20190821-81645-19pboop.pdf","download_url":"https://www.academia.edu/attachments/60355929/download_file?st=MTczMjQ5NDE4MCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Lexical_concreteness_in_narrative.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/60355929/Lexical_concreteness_in_narrative__2019_20190821-81645-19pboop-libre.pdf?1566406235=\u0026response-content-disposition=attachment%3B+filename%3DLexical_concreteness_in_narrative.pdf\u0026Expires=1732474251\u0026Signature=N-PzOZOTgA7KpxomufM6HJ7kd9emvcmIBud71jD0QSPnzr4VV27X96JI1~RtNwn-SOF4awM1QuAhSfRF2dAQHkUaKN-tMNl1wlfmw-bT3xI2KCxvaY6nOvGL~bTCs5PcvWoVeoWH~7vB3xdInu0Gf7gtHE0nHM87W-1rzt8u3ZkG64XDuxWEjc8GSerEvyWRwiJL4moBo2xOehwQ5mkNpShtL7ukqpgz69oOlDZoC7eKXu5ppg~KT5nWOBPq878~aNTl01KL8fJy5B0Ua6~QWAugdwlyu3bINOd-64I3xWQht7EBhICBjJ48P8lpA1HUcolFQKD8gaiwPkg~MK6TjQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":2852,"name":"Narrative","url":"https://www.academia.edu/Documents/in/Narrative"},{"id":5513,"name":"Narratology","url":"https://www.academia.edu/Documents/in/Narratology"},{"id":6492,"name":"Storytelling","url":"https://www.academia.edu/Documents/in/Storytelling"},{"id":13769,"name":"Lexical Semantics","url":"https://www.academia.edu/Documents/in/Lexical_Semantics"},{"id":15674,"name":"Linguistics","url":"https://www.academia.edu/Documents/in/Linguistics"},{"id":18880,"name":"Narrative Analysis","url":"https://www.academia.edu/Documents/in/Narrative_Analysis"},{"id":19818,"name":"Narrative Theory","url":"https://www.academia.edu/Documents/in/Narrative_Theory"},{"id":49267,"name":"Computational Linguistics \u0026 NLP","url":"https://www.academia.edu/Documents/in/Computational_Linguistics_and_NLP"},{"id":332649,"name":"Concreteness","url":"https://www.academia.edu/Documents/in/Concreteness"}],"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="40139377"><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/40139377/A_Benchmark_Corpus_of_English_Misspellings_and_a_Minimally_supervised_Model_for_Spelling_Correction"><img alt="Research paper thumbnail of A Benchmark Corpus of English Misspellings and a Minimally-supervised Model for Spelling Correction" class="work-thumbnail" src="https://attachments.academia-assets.com/60355773/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/40139377/A_Benchmark_Corpus_of_English_Misspellings_and_a_Minimally_supervised_Model_for_Spelling_Correction">A Benchmark Corpus of English Misspellings and a Minimally-supervised Model for Spelling Correction</a></div><div class="wp-workCard_item"><span>Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Spelling correction has attracted a lot of attention in the NLP community. However, models have b...</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">Spelling correction has attracted a lot of attention in the NLP community. However, models have been usually evaluated on artificially-created or proprietary corpora. A publicly-available corpus of authentic misspellings, annotated in context, is still lacking. To address this, we present and release an annotated data set of 6,121 spelling errors in context, based on a corpus of essays written by English language learners. We also develop a minimally-supervised context-aware approach to spelling correction. It achieves strong results on our data: 88.12% accuracy. This approach can also train with a minimal amount of annotated data (performance reduced by less than 1%). Furthermore, this approach allows easy porta-bility to new domains. We evaluate our model on data from a medical domain and demonstrate that it rivals the performance of a model trained and tuned on in-domain data.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="eb854d7283bbe5731f08b8cc52c5755c" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":60355773,"asset_id":40139377,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/60355773/download_file?st=MTczMjQ5NDE4MCw4LjIyMi4yMDguMTQ2&st=MTczMjQ5NDE4MCw4LjIyMi4yMDguMTQ2&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="40139377"><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="40139377"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 40139377; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=40139377]").text(description); $(".js-view-count[data-work-id=40139377]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 40139377; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='40139377']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 40139377, 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: "eb854d7283bbe5731f08b8cc52c5755c" } } $('.js-work-strip[data-work-id=40139377]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":40139377,"title":"A Benchmark Corpus of English Misspellings and a Minimally-supervised Model for Spelling Correction","translated_title":"","metadata":{"abstract":"Spelling correction has attracted a lot of attention in the NLP community. 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class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/40138988/How_to_account_for_mispellings_Quantifying_the_benefit_of_character_representations_in_neural_content_scoring_models">How to account for mispellings: Quantifying the benefit of character representations in neural content scoring models</a></div><div class="wp-workCard_item"><span>Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Character-based representations in neural models have been claimed to be a tool to overcome spell...</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">Character-based representations in neural models have been claimed to be a tool to overcome spelling variation in word token-based input. We examine this claim in neural models for content scoring. We formulate precise hypotheses about the possible effects of adding character representations to word-based models and test these hypotheses on large-scale real-world content scoring datasets. We find that, while character representations may provide small performance gains in general, their effectiveness in accounting for spelling variation may be limited. We show that spelling correction can provide larger gains than character representations, and that spelling correction improves the performance of models with character representations. With these insights, we report a new state of the art on the ASAP-SAS short content scoring dataset.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="50c3a54849b997570e94be65a9310e99" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":60355344,"asset_id":40138988,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/60355344/download_file?st=MTczMjQ5NDE4MCw4LjIyMi4yMDguMTQ2&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="40138988"><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="40138988"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 40138988; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=40138988]").text(description); $(".js-view-count[data-work-id=40138988]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 40138988; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='40138988']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 40138988, 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: "50c3a54849b997570e94be65a9310e99" } } $('.js-work-strip[data-work-id=40138988]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":40138988,"title":"How to account for mispellings: Quantifying the benefit of character representations in neural content scoring models","translated_title":"","metadata":{"abstract":"Character-based representations in neural models have been claimed to be a tool to overcome spelling variation in word token-based input. We examine this claim in neural models for content scoring. We formulate precise hypotheses about the possible effects of adding character representations to word-based models and test these hypotheses on large-scale real-world content scoring datasets. We find that, while character representations may provide small performance gains in general, their effectiveness in accounting for spelling variation may be limited. We show that spelling correction can provide larger gains than character representations, and that spelling correction improves the performance of models with character representations. 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