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data-section="Papers" id="Papers"><h3 class="profile--tab_heading_container">Papers by Patrick Saint-dizier</h3></div><div class="js-work-strip profile--work_container" data-work-id="123619164"><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/123619164/Modelling_Human_Computer_Interactions_in_a_Friendly_Interface"><img alt="Research paper thumbnail of Modelling Human-Computer Interactions in a Friendly Interface" class="work-thumbnail" src="https://attachments.academia-assets.com/118008545/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/123619164/Modelling_Human_Computer_Interactions_in_a_Friendly_Interface">Modelling Human-Computer Interactions in a Friendly 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data-click-track="profile-work-strip-title" href="https://www.academia.edu/123619163/Processing_Discourse_in_Dislog_on_TextCoop">Processing Discourse in Dislog on TextCoop</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">International audienceThis demo presents the TextCoop platform and the Dislog language, based on ...</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">International audienceThis demo presents the TextCoop platform and the Dislog language, based on logic program-ming, which have primarily been designed for discourse processing. The linguistic architectureand the basics of discourse analysis in TextCoop are introduced. Application demos include:argument mining in opinon texts, dialog analysis, and procedural and requirement texts analysis.Via prototypes in the industry, this framework has now reached the TRL5 level</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="83570578295b7c6319f40e362493fdc6" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":118008543,"asset_id":123619163,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/118008543/download_file?st=MTczMjc5MjgyMSw4LjIyMi4yMDguMTQ2&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="123619163"><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="123619163"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 123619163; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=123619163]").text(description); $(".js-view-count[data-work-id=123619163]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 123619163; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='123619163']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 123619163, 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: "83570578295b7c6319f40e362493fdc6" } } $('.js-work-strip[data-work-id=123619163]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":123619163,"title":"Processing Discourse in Dislog on TextCoop","translated_title":"","metadata":{"abstract":"International audienceThis demo presents the TextCoop platform and the Dislog language, based on logic program-ming, which have primarily been designed for discourse processing. 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A method is proposed, based on natural language processing technology, to improve requirement production and writing. This method is based on the notion of correction memory and correction patterns derived from the observation of the corrections made by technical writers. This work remains preliminary and exploratory. 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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="118917416"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/118917416/Proceedings_of_the_2009_Workshop_on_Knowledge_and_Reasoning_for_Answering_Questions"><img alt="Research paper thumbnail of Proceedings of the 2009 Workshop on Knowledge and Reasoning for Answering Questions" 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" rel="nofollow" href="https://www.academia.edu/118917416/Proceedings_of_the_2009_Workshop_on_Knowledge_and_Reasoning_for_Answering_Questions">Proceedings of the 2009 Workshop on Knowledge and Reasoning for Answering Questions</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The introduction of reasoning capabilities in question-answering (QA) systems appeared in the lat...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The introduction of reasoning capabilities in question-answering (QA) systems appeared in the late 70s. A second generation of QA systems, aimed at being cooperative, emerged in the late 80s - early 90s. In these systems, quite advanced reasoning models were developed on closed domains to go beyond the production of direct responses to a query, in particular when the query has no response or when it contains misconceptions. More recently, systems such as JAVELIN, Inference WEB or Cogex, operating over open domains, gradually integrated inferential components, but not as advanced as those of the 90s. Performances of these systems in the recent TREC-QA tracks show that reasoning components substantially improve the response relevance and accuracy. They can also potentially be much more cooperative. However, there is still a long way before being able to produce accurate, cooperative and robust QA systems, because of the very large complexity of natural systems and of the need to make several communities work together on common grounds. Recent foundational, methodological and technological developments in knowledge representation (e.g. ontologies, knowledge bases incorporating various forms of incompleteness or uncertainty), in advanced reasoning forms (e.g. data fusion-integration, argumentation, decision theory, fuzzy logic, incomplete knowledge bases, etc.), in advanced language processing resources and techniques (for question processing as well as for generating responses) including semantic role labelling and the recognition and resolution of temporal and spatial expressions, and recent progress in HLT and formal pragmatics (user models, intentions, etc.) make it possible to foresee the elaboration of much more accurate, cooperative and robust systems dedicated to answering questions from multimedia supports or from textual data, from e.g. online texts or web pages, operating either on open or closed domains. The user interface aspects (input, output (e.g. SMS or advanced interfaces), on line help, dialogue, etc.) are also crucial for the viability of such systems.</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="118917416"><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="118917416"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 118917416; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=118917416]").text(description); $(".js-view-count[data-work-id=118917416]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 118917416; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='118917416']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 118917416, 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" } } $('.js-work-strip[data-work-id=118917416]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":118917416,"title":"Proceedings of the 2009 Workshop on Knowledge and Reasoning for Answering Questions","translated_title":"","metadata":{"abstract":"The introduction of reasoning capabilities in question-answering (QA) systems appeared in the late 70s. 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Performances of these systems in the recent TREC-QA tracks show that reasoning components substantially improve the response relevance and accuracy. They can also potentially be much more cooperative. However, there is still a long way before being able to produce accurate, cooperative and robust QA systems, because of the very large complexity of natural systems and of the need to make several communities work together on common grounds. 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Automatically identifying the structure of reasoning from natural language is extremely demanding. Our hypothesis is that the structure of dialogue can yield additional clues as to argument structures that are created and cocreated. 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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="123619153"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/123619153/Musical_Rhetoric"><img alt="Research paper thumbnail of Musical Rhetoric" 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" rel="nofollow" href="https://www.academia.edu/123619153/Musical_Rhetoric">Musical Rhetoric</a></div><div class="wp-workCard_item"><span>Musical Rhetoric</span><span>, 2014</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This book explores the various roles played by music in a rhetoric discourse or in an argumentati...</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 book explores the various roles played by music in a rhetoric discourse or in an argumentative construction. Music turns out to be a very strong, persuasive and expressive means of great interest for rhetoric. Its association with a text or, more generally, with conceptual or psychological content is of great interest and importance as an intellectual consideration, and also in a number of everyday-life aspects such as TV news and advertising, shopping mall atmosphere and movie music.</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="123619153"><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="123619153"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 123619153; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=123619153]").text(description); $(".js-view-count[data-work-id=123619153]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 123619153; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='123619153']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 123619153, 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" } } $('.js-work-strip[data-work-id=123619153]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":123619153,"title":"Musical Rhetoric","translated_title":"","metadata":{"abstract":"This book explores the various roles played by music in a rhetoric discourse or in an argumentative construction. 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A method is proposed, based on natural language processing technology, to improve requirement production and writing. This method is based on the notion of correction memory and correction patterns derived from the observation of the corrections made by technical writers. This work remains preliminary and exploratory. We address in this article the case of fuzzy expressions and of complex sentences, which are major errors found in technical documentation.</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="123619152"><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="123619152"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 123619152; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=123619152]").text(description); $(".js-view-count[data-work-id=123619152]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 123619152; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='123619152']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 123619152, 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" } } $('.js-work-strip[data-work-id=123619152]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":123619152,"title":"An Approach to Improve the Language Quality of Requirements","translated_title":"","metadata":{"abstract":"In this article, requirement authoring methods are investigated together with the way they impact the tasks carried out by technical writers. 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Experience shows that even with several levels of proofreading and validation, most texts still contain a large number of language errors (lexical, grammatical, style, business, w.r.t. authoring recommendations), and lack of overall cohesion and coherence. LELIE [a] has been designed to track these errors and, whenever possible, to suggest corrections. LELIE has obviously an impact on the technical writer behavior: LELIE rapidly becomes an essential and user-friendly authoring companion.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="55d7351e8a0d100a2ca55be9f912d1ec" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":118008504,"asset_id":123619098,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/118008504/download_file?st=MTczMjc5MjgyMSw4LjIyMi4yMDguMTQ2&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="123619098"><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="123619098"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 123619098; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=123619098]").text(description); $(".js-view-count[data-work-id=123619098]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 123619098; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='123619098']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 123619098, 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: "55d7351e8a0d100a2ca55be9f912d1ec" } } $('.js-work-strip[data-work-id=123619098]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":123619098,"title":"LELIE - An Intelligent Assistant for Improving Requirement Authoring","translated_title":"","metadata":{"abstract":"When writing or revising a set of requirements, or any technical document, it is particularly challenging to make sure that texts read easily and are unambiguous for any domain actor. 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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="118917416"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/118917416/Proceedings_of_the_2009_Workshop_on_Knowledge_and_Reasoning_for_Answering_Questions"><img alt="Research paper thumbnail of Proceedings of the 2009 Workshop on Knowledge and Reasoning for Answering Questions" 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" rel="nofollow" href="https://www.academia.edu/118917416/Proceedings_of_the_2009_Workshop_on_Knowledge_and_Reasoning_for_Answering_Questions">Proceedings of the 2009 Workshop on Knowledge and Reasoning for Answering Questions</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The introduction of reasoning capabilities in question-answering (QA) systems appeared in the lat...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The introduction of reasoning capabilities in question-answering (QA) systems appeared in the late 70s. A second generation of QA systems, aimed at being cooperative, emerged in the late 80s - early 90s. In these systems, quite advanced reasoning models were developed on closed domains to go beyond the production of direct responses to a query, in particular when the query has no response or when it contains misconceptions. More recently, systems such as JAVELIN, Inference WEB or Cogex, operating over open domains, gradually integrated inferential components, but not as advanced as those of the 90s. Performances of these systems in the recent TREC-QA tracks show that reasoning components substantially improve the response relevance and accuracy. They can also potentially be much more cooperative. However, there is still a long way before being able to produce accurate, cooperative and robust QA systems, because of the very large complexity of natural systems and of the need to make several communities work together on common grounds. Recent foundational, methodological and technological developments in knowledge representation (e.g. ontologies, knowledge bases incorporating various forms of incompleteness or uncertainty), in advanced reasoning forms (e.g. data fusion-integration, argumentation, decision theory, fuzzy logic, incomplete knowledge bases, etc.), in advanced language processing resources and techniques (for question processing as well as for generating responses) including semantic role labelling and the recognition and resolution of temporal and spatial expressions, and recent progress in HLT and formal pragmatics (user models, intentions, etc.) make it possible to foresee the elaboration of much more accurate, cooperative and robust systems dedicated to answering questions from multimedia supports or from textual data, from e.g. online texts or web pages, operating either on open or closed domains. The user interface aspects (input, output (e.g. SMS or advanced interfaces), on line help, dialogue, etc.) are also crucial for the viability of such systems.</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="118917416"><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="118917416"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 118917416; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=118917416]").text(description); $(".js-view-count[data-work-id=118917416]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 118917416; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='118917416']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 118917416, 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" } } $('.js-work-strip[data-work-id=118917416]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":118917416,"title":"Proceedings of the 2009 Workshop on Knowledge and Reasoning for Answering Questions","translated_title":"","metadata":{"abstract":"The introduction of reasoning capabilities in question-answering (QA) systems appeared in the late 70s. 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Performances of these systems in the recent TREC-QA tracks show that reasoning components substantially improve the response relevance and accuracy. They can also potentially be much more cooperative. However, there is still a long way before being able to produce accurate, cooperative and robust QA systems, because of the very large complexity of natural systems and of the need to make several communities work together on common grounds. 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Automatically identifying the structure of reasoning from natural language is extremely demanding. Our hypothesis is that the structure of dialogue can yield additional clues as to argument structures that are created and cocreated. 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