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class="suggested-user-card__user-info__subheader ds2-5-body-xs">Drexel University</p></div></div></ul></div><div class="ri-section"><div class="ri-section-header"><span>Interests</span></div><div class="ri-tags-container"><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="1389" href="https://www.academia.edu/Documents/in/Computer_Science"><div id="js-react-on-rails-context" style="display:none" data-rails-context="{"inMailer":false,"i18nLocale":"en","i18nDefaultLocale":"en","href":"https://umass.academia.edu/WBruceCroft","location":"/WBruceCroft","scheme":"https","host":"umass.academia.edu","port":null,"pathname":"/WBruceCroft","search":null,"httpAcceptLanguage":null,"serverSide":false}"></div> <div class="js-react-on-rails-component" style="display:none" data-component-name="Pill" data-props="{"color":"gray","children":["Computer Science"]}" data-trace="false" data-dom-id="Pill-react-component-1946fb94-f0eb-4a52-9632-7c9b3fe8a4e4"></div> <div 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Bruce Croft</h3></div><div class="js-work-strip profile--work_container" data-work-id="2788025"><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/2788025/Northeast_Artificial_Intelligence_Consortium_NAIC_Volume_6_Building_an_Intelligent_Assistant_The_Acquisition_Integration_and_Maintenance_of_Complex_Distributed_Tasks"><img alt="Research paper thumbnail of Northeast Artificial Intelligence Consortium (NAIC). Volume 6. Building an Intelligent Assistant: The Acquisition, Integration, and Maintenance of Complex Distributed Tasks" 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/2788025/Northeast_Artificial_Intelligence_Consortium_NAIC_Volume_6_Building_an_Intelligent_Assistant_The_Acquisition_Integration_and_Maintenance_of_Complex_Distributed_Tasks">Northeast Artificial Intelligence Consortium (NAIC). Volume 6. Building an Intelligent Assistant: The Acquisition, Integration, and Maintenance of Complex Distributed Tasks</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract: The Northeast Artificial Intelligence Consortium (NAIC) was created by the Air Force Sy...</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">Abstract: The Northeast Artificial Intelligence Consortium (NAIC) was created by the Air Force Systems Command, Rome Air Development Center, and the Office of Scientific Research. Its purpose was to conduct pertinent research in artificial intelligence and to perform activities ancillary to this research. This report describes progress during the existence of the NAIC on the technical research tasks undertaken at the member universities.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e751ee06b564551b1ccdb73c9eac0f39" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":30740565,"asset_id":2788025,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/30740565/download_file?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="2788025"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2788025"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2788025; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2788025]").text(description); $(".js-view-count[data-work-id=2788025]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2788025; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2788025']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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: "e751ee06b564551b1ccdb73c9eac0f39" } } $('.js-work-strip[data-work-id=2788025]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2788025,"title":"Northeast Artificial Intelligence Consortium (NAIC). 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It is one of the simplest applications of natural language processing to IR, and one of the most e ective in terms of user acceptance and consistent, though small, retrieval improvements. Current stemming techniques do not, however, re ect the language use in speci c corpora and this can lead to occasional serious retrieval failures. We propose a technique for using corpus-based word variant co-occurrence statistics to modify or create a stemmer. The experimental results generated using English newspaper and legal text and Spanish text demonstrate the viability of this technique and its advantages relative to conventional approaches.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="32e79e5d0f348e914eb83163456dd95f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":30740561,"asset_id":2788024,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/30740561/download_file?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="2788024"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2788024"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2788024; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2788024]").text(description); $(".js-view-count[data-work-id=2788024]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2788024; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2788024']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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: "32e79e5d0f348e914eb83163456dd95f" } } $('.js-work-strip[data-work-id=2788024]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2788024,"title":"Corpus-Based Stemming using Co-occurrence of Word","internal_url":"https://www.academia.edu/2788024/Corpus_Based_Stemming_using_Co_occurrence_of_Word","owner_id":1389,"coauthors_can_edit":true,"owner":{"id":1389,"first_name":"W. 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Bruce Croft","url":"https://umass.academia.edu/WBruceCroft"},"attachments":[{"id":30740561,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://a.academia-assets.com/images/blank-paper.jpg","file_name":"10.1.1.35.604.pdf","download_url":"https://www.academia.edu/attachments/30740561/download_file","bulk_download_file_name":"Corpus_Based_Stemming_using_Co_occurrenc.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/30740561/10.1.1.35.604-libre.pdf?1392055682=\u0026response-content-disposition=attachment%3B+filename%3DCorpus_Based_Stemming_using_Co_occurrenc.pdf\u0026Expires=1740055929\u0026Signature=SdCJjbvMVQ5o~SO647pe4nqfKqfDb0VosjGmDeKmVwNZCsMDUCmpCBPifksKsNmXT4DzBPN19Z2GKzzh4rZZfUAOz82ozVuODrw5hg3Q9cT0z3EVO96xch~Fw9AqVudMu~oI2eGNityeppLIZv5bG7qBYDxZL8j-3WQNTcz0dEY-cP3VisHTR39IQaP2InGFCbydLVsyzGVaHTU5xgY7Lupawc4hloUglI8TfzuHvhuFwGrH0sAP9T2cn3Urlav~0JZvnwsvFSi7x4TjEEnDHXHnTpGGR3Hljo6rFJWtc~GRcg3Qc66oIe3kQORQ8kDDdSpcOJocfSHghLSlJEogsQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, 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="2788023"><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/2788023/Hierarchical_language_models_for_expert_finding_in_enterprise_corpora"><img alt="Research paper thumbnail of Hierarchical language models for expert finding in enterprise corpora" 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/2788023/Hierarchical_language_models_for_expert_finding_in_enterprise_corpora">Hierarchical language models for expert finding in enterprise corpora</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Enterprise corpora contain evidence of what employees work on and therefore can be used to automa...</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">Enterprise corpora contain evidence of what employees work on and therefore can be used to automatically find experts on a given topic. We present a general approach for representing the knowledge of a potential expert as a mixture of language models from associated documents. First we retrieve documents given the expert's name using a generative probabilistic technique and weight the retrieved documents according to expert-specific posterior distribution.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="7a01a546d7870904300310c78f813f6b" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":30838833,"asset_id":2788023,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/30838833/download_file?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="2788023"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2788023"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2788023; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2788023]").text(description); $(".js-view-count[data-work-id=2788023]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2788023; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2788023']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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: "7a01a546d7870904300310c78f813f6b" } } $('.js-work-strip[data-work-id=2788023]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2788023,"title":"Hierarchical language models for expert finding in enterprise corpora","internal_url":"https://www.academia.edu/2788023/Hierarchical_language_models_for_expert_finding_in_enterprise_corpora","owner_id":1389,"coauthors_can_edit":true,"owner":{"id":1389,"first_name":"W. 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Simple models make it difficult to accurately model a user's information need. The model presented in the paper is based on Markov random fields and allows almost arbitrary features to be encoded. This provides a powerful mechanism for modeling many of the implicit constraints a user has in mind when formulating a query.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="629890029babb5f4c5a66148a87a4f65" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":30838829,"asset_id":2788022,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/30838829/download_file?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="2788022"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2788022"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2788022; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2788022]").text(description); $(".js-view-count[data-work-id=2788022]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2788022; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2788022']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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: "629890029babb5f4c5a66148a87a4f65" } } $('.js-work-strip[data-work-id=2788022]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2788022,"title":"Beyond bags of words: Modeling implicit user preferences in information retrieval","internal_url":"https://www.academia.edu/2788022/Beyond_bags_of_words_Modeling_implicit_user_preferences_in_information_retrieval","owner_id":1389,"coauthors_can_edit":true,"owner":{"id":1389,"first_name":"W. Bruce","middle_initials":null,"last_name":"Croft","page_name":"WBruceCroft","domain_name":"umass","created_at":"2008-08-13T11:28:23.544-07:00","display_name":"W. 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Compared to system memory, disk bandwidth is poor, and seek times are worse. We circumvent this problem by considering query evaluation strategies in main memory. We show how new accumulator trimming techniques combined with inverted list skipping can produce extremely high performance retrieval systems without resorting to methods that may harm effectiveness.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="07969388f8a1e2899b91d2cdaec797e4" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":30740554,"asset_id":2788021,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/30740554/download_file?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="2788021"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2788021"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2788021; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2788021]").text(description); $(".js-view-count[data-work-id=2788021]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2788021; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2788021']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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: "07969388f8a1e2899b91d2cdaec797e4" } } $('.js-work-strip[data-work-id=2788021]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2788021,"title":"Efficient document retrieval in main memory","internal_url":"https://www.academia.edu/2788021/Efficient_document_retrieval_in_main_memory","owner_id":1389,"coauthors_can_edit":true,"owner":{"id":1389,"first_name":"W. 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In particular, Boolean query operators are conveniently modeled as link matrices of the Bayesian Network. Prior work has shown, however, that these operators do not perform as well as the pnorm operators used for modeling query operators in the context of the vector space model. This motivates the search for alternative probabilistic formulations for these operators.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="975cf95bb6e07d1dc21521c198bcae31" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":31008972,"asset_id":2788020,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/31008972/download_file?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="2788020"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2788020"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2788020; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2788020]").text(description); $(".js-view-count[data-work-id=2788020]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2788020; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2788020']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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: "975cf95bb6e07d1dc21521c198bcae31" } } $('.js-work-strip[data-work-id=2788020]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2788020,"title":"PIC matrices: A computationally tractable class of probabilistic query operators","internal_url":"https://www.academia.edu/2788020/PIC_matrices_A_computationally_tractable_class_of_probabilistic_query_operators","owner_id":1389,"coauthors_can_edit":true,"owner":{"id":1389,"first_name":"W. Bruce","middle_initials":null,"last_name":"Croft","page_name":"WBruceCroft","domain_name":"umass","created_at":"2008-08-13T11:28:23.544-07:00","display_name":"W. Bruce Croft","url":"https://umass.academia.edu/WBruceCroft"},"attachments":[{"id":31008972,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/31008972/thumbnails/1.jpg","file_name":"pic_matrices_a_computationally_tractable_class_of_probabilistic_query_operators.pdf","download_url":"https://www.academia.edu/attachments/31008972/download_file","bulk_download_file_name":"PIC_matrices_A_computationally_tractable.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/31008972/pic_matrices_a_computationally_tractable_class_of_probabilistic_query_operators-libre.pdf?1392251201=\u0026response-content-disposition=attachment%3B+filename%3DPIC_matrices_A_computationally_tractable.pdf\u0026Expires=1740055929\u0026Signature=bkN5sozrd1dgYjvL9P7pOSySMbM8hNnecoIIQPiWFm4YpH6LabzDcUknWCj38tX4G9ZzX0rR6jua5AdYwicO~GHelyibsMHZzb2JIsRXtzzvf39VNDdIEGjz6Wu1RXxb~CQsxGxbCkeBqXaFvz7twaoAXlYtfEzZfj5GrEyPY2fy~Yw0JaYAvZjEJ7kO0cfkH8U9ykoJPkMG9H72NmCxfry3D3EjKpPmEyCPRLvO3iRr1Hg0FLbrAGejb-fzzbt4WYbuhkSfwW0C3j4YETN~PGAy-xfEtsievzh~LdXYFNINMvraZ3VvHmREoCk0kSlQzNrp5BrNU~adNvHEldFkWA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, 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="2788019"><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/2788019/Parameterized_concept_weighting_in_verbose_queries"><img alt="Research paper thumbnail of Parameterized concept weighting in verbose queries" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/2788019/Parameterized_concept_weighting_in_verbose_queries">Parameterized concept weighting in verbose queries</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract The majority of the current information retrieval models weight the query concepts (eg, ...</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">Abstract The majority of the current information retrieval models weight the query concepts (eg, terms or phrases) in an unsupervised manner, based solely on the collection statistics. In this paper, we go beyond the unsupervised estimation of concept weights, and propose a parameterized concept weighting model. In our model, the weight of each query concept is determined using a parameterized combination of diverse importance features.</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="2788019"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2788019"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2788019; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2788019]").text(description); $(".js-view-count[data-work-id=2788019]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2788019; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2788019']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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" } } $('.js-work-strip[data-work-id=2788019]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2788019,"title":"Parameterized concept weighting in verbose queries","internal_url":"https://www.academia.edu/2788019/Parameterized_concept_weighting_in_verbose_queries","owner_id":1389,"coauthors_can_edit":true,"owner":{"id":1389,"first_name":"W. Bruce","middle_initials":null,"last_name":"Croft","page_name":"WBruceCroft","domain_name":"umass","created_at":"2008-08-13T11:28:23.544-07:00","display_name":"W. Bruce Croft","url":"https://umass.academia.edu/WBruceCroft"},"attachments":[]}, 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="2788018"><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/2788018/Probabilistic_retrieval_of_ocr_degraded_text_using_n_grams"><img alt="Research paper thumbnail of Probabilistic retrieval of ocr degraded text using n-grams" 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/2788018/Probabilistic_retrieval_of_ocr_degraded_text_using_n_grams">Probabilistic retrieval of ocr degraded text using n-grams</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The retrieval of OCR degraded text using n-gram formulations within a probabilistic retrieval sys...</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 retrieval of OCR degraded text using n-gram formulations within a probabilistic retrieval system is examined in this paper. Direct retrieval of documents using n-gram databases of 2 and 3-grams or 2, 3, 4 and 5-grams resulted in improved retrieval performance over standard (word based) queries on the same data when a level of 10 percent degradation or worse was achieved.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="8f218f0c21e6b451b5391e3f67853974" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":30740569,"asset_id":2788018,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/30740569/download_file?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="2788018"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2788018"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2788018; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2788018]").text(description); $(".js-view-count[data-work-id=2788018]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2788018; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2788018']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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: "8f218f0c21e6b451b5391e3f67853974" } } $('.js-work-strip[data-work-id=2788018]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2788018,"title":"Probabilistic retrieval of ocr degraded text using n-grams","internal_url":"https://www.academia.edu/2788018/Probabilistic_retrieval_of_ocr_degraded_text_using_n_grams","owner_id":1389,"coauthors_can_edit":true,"owner":{"id":1389,"first_name":"W. 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Bruce Croft","url":"https://umass.academia.edu/WBruceCroft"},"attachments":[{"id":30740569,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://a.academia-assets.com/images/blank-paper.jpg","file_name":"Probabilistic_Retrieval_of_OCR_Degraded_Text.pdf","download_url":"https://www.academia.edu/attachments/30740569/download_file","bulk_download_file_name":"Probabilistic_retrieval_of_ocr_degraded.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/30740569/Probabilistic_Retrieval_of_OCR_Degraded_Text-libre.pdf?1392098466=\u0026response-content-disposition=attachment%3B+filename%3DProbabilistic_retrieval_of_ocr_degraded.pdf\u0026Expires=1740055929\u0026Signature=f0b7Ar8qdSzSF0t2nYcxF-JM5yCQxzb3LOEBEGbO17zXAgtVZrYj9z85i5ZC5lxtaNo94v3u48GsG7rMyO~l-3R~fsvfrZa~d6Yb9Lo8aHaWvw1k9RkzqOP5EdJnj4Zgb7KR3SwxiRZDhR4XtfLU4mcQVsGv~CgfMrvakkHj2~1X2z1A7Z35DkFqrP4yd2Ib86zd0OaaoQV76P6EYdKuptgDjoOILNWzPIa18HiU5mO9uPYbaQVOySgOXhwLBITQ0lg~INsCsC5RYprL3-6ECEi68kpdqHd3BB2Sk4O-MQVjxTFJwnG84Ts5fBA~nFD54S5cbhDd7K854MMdTyovvw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, 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="2788017"><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/2788017/Novelty_detection_based_on_sentence_level_patterns"><img alt="Research paper thumbnail of Novelty detection based on sentence level patterns" 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/2788017/Novelty_detection_based_on_sentence_level_patterns">Novelty detection based on sentence level patterns</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract The detection of new information in a document stream is an important component of many ...</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">Abstract The detection of new information in a document stream is an important component of many potential applications. In this paper, a new novelty detection approach based on the identification of sentence level patterns is proposed. Given a user's information need, some patterns in sentences such as combinations of query words, named entities and phrases, may contain more important and relevant information than single words.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="57d91c0544f5c760a24d76a623874afb" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":30740520,"asset_id":2788017,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/30740520/download_file?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="2788017"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2788017"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2788017; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2788017]").text(description); $(".js-view-count[data-work-id=2788017]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2788017; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2788017']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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: "57d91c0544f5c760a24d76a623874afb" } } $('.js-work-strip[data-work-id=2788017]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2788017,"title":"Novelty detection based on sentence level patterns","internal_url":"https://www.academia.edu/2788017/Novelty_detection_based_on_sentence_level_patterns","owner_id":1389,"coauthors_can_edit":true,"owner":{"id":1389,"first_name":"W. Bruce","middle_initials":null,"last_name":"Croft","page_name":"WBruceCroft","domain_name":"umass","created_at":"2008-08-13T11:28:23.544-07:00","display_name":"W. 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Bruce","middle_initials":null,"last_name":"Croft","page_name":"WBruceCroft","domain_name":"umass","created_at":"2008-08-13T11:28:23.544-07:00","display_name":"W. Bruce Croft","url":"https://umass.academia.edu/WBruceCroft"},"attachments":[]}, 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="2788015"><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/2788015/Evaluating_search_in_personal_social_media_collections"><img alt="Research paper thumbnail of Evaluating search in personal social media collections" 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/2788015/Evaluating_search_in_personal_social_media_collections">Evaluating search in personal social media collections</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract The prevalence of social media applications is generating potentially large personal arc...</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">Abstract The prevalence of social media applications is generating potentially large personal archives of posts, tweets, and other communications. The existence of these archives creates a need for search tools, which can be seen as an extension of current desktop search services. Little is currently known about the best search techniques for personal archives of social data, because of the difficulty of creating test collections.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="8173561b94c7107f93fecf467078afaf" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":30740489,"asset_id":2788015,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/30740489/download_file?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="2788015"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2788015"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2788015; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2788015]").text(description); $(".js-view-count[data-work-id=2788015]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2788015; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2788015']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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: "8173561b94c7107f93fecf467078afaf" } } $('.js-work-strip[data-work-id=2788015]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2788015,"title":"Evaluating search in personal social media collections","internal_url":"https://www.academia.edu/2788015/Evaluating_search_in_personal_social_media_collections","owner_id":1389,"coauthors_can_edit":true,"owner":{"id":1389,"first_name":"W. Bruce","middle_initials":null,"last_name":"Croft","page_name":"WBruceCroft","domain_name":"umass","created_at":"2008-08-13T11:28:23.544-07:00","display_name":"W. 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The advances achieved by information retrieval researchers from the 1950s through to the present day are detailed next, focusing on the process of locating relevant information. The paper closes with speculation on where the future of information retrieval lies.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="99fb67474ff834ee2703deba2cc3d638" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":30740491,"asset_id":2788014,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/30740491/download_file?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="2788014"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2788014"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2788014; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2788014]").text(description); $(".js-view-count[data-work-id=2788014]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2788014; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2788014']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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: "99fb67474ff834ee2703deba2cc3d638" } } $('.js-work-strip[data-work-id=2788014]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2788014,"title":"The History of Information Retrieval Research","internal_url":"https://www.academia.edu/2788014/The_History_of_Information_Retrieval_Research","owner_id":1389,"coauthors_can_edit":true,"owner":{"id":1389,"first_name":"W. 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In particular, it...</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">Abstract The TIPSTER collection is unusual because of both its size and detail. In particular, it describes a set of information needs, as opposed to traditional queries. These detailed representations of information need are an opportunity for research on different methods of formulating queries. This paper describes several methods of constructing queries for the INQUERY information retrieval system, and then evaluates those methods on the TIPSTER document collection. Both AdHoc and Routing query processing methods are evaluated.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="49a4c58c23fbcdb5f17c9dba94d45952" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":30889753,"asset_id":2788013,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/30889753/download_file?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="2788013"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2788013"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2788013; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2788013]").text(description); $(".js-view-count[data-work-id=2788013]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2788013; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2788013']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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: "49a4c58c23fbcdb5f17c9dba94d45952" } } $('.js-work-strip[data-work-id=2788013]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2788013,"title":"An evaluation of query processing strategies using the TIPSTER collection","internal_url":"https://www.academia.edu/2788013/An_evaluation_of_query_processing_strategies_using_the_TIPSTER_collection","owner_id":1389,"coauthors_can_edit":true,"owner":{"id":1389,"first_name":"W. 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Bruce Croft","url":"https://umass.academia.edu/WBruceCroft"},"attachments":[{"id":30889753,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/30889753/thumbnails/1.jpg","file_name":"an_evaluation_of_query_processing_strategies_using_the_tipster_collection.pdf","download_url":"https://www.academia.edu/attachments/30889753/download_file","bulk_download_file_name":"An_evaluation_of_query_processing_strate.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/30889753/an_evaluation_of_query_processing_strategies_using_the_tipster_collection-libre.pdf?1392161369=\u0026response-content-disposition=attachment%3B+filename%3DAn_evaluation_of_query_processing_strate.pdf\u0026Expires=1740055930\u0026Signature=dPpd9y4PZo1gIN2x8l7dESin3Mgzx5CqZoSClMRBS1MarY2pObXL4EXSEpnPrEDqvjecrRLv7gk9KVhzBV0K2u-ZHvS3V65PZ-NVfsnc82lcK~4nfB85UipePTSi7EugY9OOT8SJkQkpn5rp1Ev3298zPaPBtPkS76N1jYW0NBFmT8FCFpMIsSzhoX1bTG~ATDJaeuFRKisbtrUbgpwvGP8j1EgAmLM2CFaK~HOD-grOutcLWI4PF1JetSy7T93qHfC3HYHSY8l3T6lQLIPgYGr10WEzBxGKMrXhnTd58H5rShZi27k0Ic80TcGsp~vlDmxj0~c9kE7MzX2BGxpO4w__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, 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="2788012"><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/2788012/Building_a_semantic_representation_for_personal_information"><img alt="Research paper thumbnail of Building a semantic representation for personal information" 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/2788012/Building_a_semantic_representation_for_personal_information">Building a semantic representation for personal information</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract A typical collection of personal information contains many documents and mentions many 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">Abstract A typical collection of personal information contains many documents and mentions many concepts (eg, person names, events, etc.). In this environment, associative browsing between these concepts and documents can be useful as a complement for search. Previous approaches in the area of semantic desktops aimed at addressing this task. However, they were not practical because they require tedious manual annotation by the user.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="23016aa3f37a3b9cb760be58412f8b0d" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":30740436,"asset_id":2788012,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/30740436/download_file?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="2788012"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2788012"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2788012; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2788012]").text(description); $(".js-view-count[data-work-id=2788012]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2788012; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2788012']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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: "23016aa3f37a3b9cb760be58412f8b0d" } } $('.js-work-strip[data-work-id=2788012]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2788012,"title":"Building a semantic representation for personal information","internal_url":"https://www.academia.edu/2788012/Building_a_semantic_representation_for_personal_information","owner_id":1389,"coauthors_can_edit":true,"owner":{"id":1389,"first_name":"W. Bruce","middle_initials":null,"last_name":"Croft","page_name":"WBruceCroft","domain_name":"umass","created_at":"2008-08-13T11:28:23.544-07:00","display_name":"W. 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Topic Detection and Tracking (TDT) is an example of such an application. In this paper we demonstrate that named entities serve as better choices of units for document representation over all words.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="34060e3340edc7cd4ce87419fcb2f211" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":30838824,"asset_id":2788011,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/30838824/download_file?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="2788011"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2788011"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2788011; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2788011]").text(description); $(".js-view-count[data-work-id=2788011]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2788011; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2788011']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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: "34060e3340edc7cd4ce87419fcb2f211" } } $('.js-work-strip[data-work-id=2788011]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2788011,"title":"Representing documents with named entities for story link detection (SLD)","internal_url":"https://www.academia.edu/2788011/Representing_documents_with_named_entities_for_story_link_detection_SLD_","owner_id":1389,"coauthors_can_edit":true,"owner":{"id":1389,"first_name":"W. 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Recently, an inference network based probabilistic retrieval model has been proposed, which views information retrieval as an evidential reasoning process in which multiple sources of evidence about document and query content are combined to estimate the relevance probabilities.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="0bb05b014ff7082e526302b789f7379f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":30740448,"asset_id":2788010,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/30740448/download_file?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="2788010"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2788010"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2788010; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2788010]").text(description); $(".js-view-count[data-work-id=2788010]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2788010; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2788010']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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: "0bb05b014ff7082e526302b789f7379f" } } $('.js-work-strip[data-work-id=2788010]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2788010,"title":"Combining automatic and manual index representations in probabilistic retrieval","internal_url":"https://www.academia.edu/2788010/Combining_automatic_and_manual_index_representations_in_probabilistic_retrieval","owner_id":1389,"coauthors_can_edit":true,"owner":{"id":1389,"first_name":"W. 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Bruce Croft","url":"https://umass.academia.edu/WBruceCroft"},"attachments":[{"id":30740448,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://a.academia-assets.com/images/blank-paper.jpg","file_name":"rajashekar95combining.pdf","download_url":"https://www.academia.edu/attachments/30740448/download_file","bulk_download_file_name":"Combining_automatic_and_manual_index_rep.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/30740448/rajashekar95combining-libre.pdf?1391834935=\u0026response-content-disposition=attachment%3B+filename%3DCombining_automatic_and_manual_index_rep.pdf\u0026Expires=1740055930\u0026Signature=GlCAkbDl8oGPO0hfXR8y7dwufLyoQkmI9h7zk0AF6~-5P6znsYDzPw2eT9NuE7vvbsjHLVphL5lcLDMqdlEYgsTWbjedtwIAcPmTuHYR~KfSCNV4~SANJAjMhFUE1qzbZvOym9lbQiY8U~rXu-TBPt6usJaF5vcv70qfLx1WG~nGVyfsAR~uCoTdP~1-O8635nlzrWfcR7w8fkPM6NVshPDmQ7n3tWAMRxMTk8WxHTu5AX8K5p~d5EthC-4Nm79N7TCcJN1ZU02LIO-FcpNzf0wX9Nd0oRu83sRXtNE3QNblly1EzP8RfXUg4jbOgTdgFygh8uySRHrme84kKpBJmQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, 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="2788009"><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/2788009/Automatic_boolean_query_suggestion_for_professional_search"><img alt="Research paper thumbnail of Automatic boolean query suggestion for professional search" 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/2788009/Automatic_boolean_query_suggestion_for_professional_search">Automatic boolean query suggestion for professional search</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract In professional search environments, such as patent search or legal search, search tasks...</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">Abstract In professional search environments, such as patent search or legal search, search tasks have unique characteristics: 1) users interactively issue several queries for a topic, and 2) users are willing to examine many retrieval results, ie, there is typically an emphasis on recall. Recent surveys have also verified that professional searchers continue to have a strong preference for Boolean queries because they provide a record of what documents were searched.</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="2788009"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2788009"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2788009; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2788009]").text(description); $(".js-view-count[data-work-id=2788009]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2788009; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2788009']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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" } } $('.js-work-strip[data-work-id=2788009]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2788009,"title":"Automatic boolean query suggestion for professional search","internal_url":"https://www.academia.edu/2788009/Automatic_boolean_query_suggestion_for_professional_search","owner_id":1389,"coauthors_can_edit":true,"owner":{"id":1389,"first_name":"W. Bruce","middle_initials":null,"last_name":"Croft","page_name":"WBruceCroft","domain_name":"umass","created_at":"2008-08-13T11:28:23.544-07:00","display_name":"W. Bruce Croft","url":"https://umass.academia.edu/WBruceCroft"},"attachments":[]}, 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="2788008"><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/2788008/Improving_the_effectiveness_of_information_retrieval_with_local_context_analysis"><img alt="Research paper thumbnail of Improving the effectiveness of information retrieval with local context analysis" 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/2788008/Improving_the_effectiveness_of_information_retrieval_with_local_context_analysis">Improving the effectiveness of information retrieval with local context analysis</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract Techniques for automatic query expansion have been extensively studied in information re...</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">Abstract Techniques for automatic query expansion have been extensively studied in information research as a means of addressing the word mismatch between queries and documents. These techniques can be categorized as either global or local. While global techniques rely on analysis of a whole collection to discover word relationships, local techniques emphasize analysis of the top-ranked documents retrieved for a query.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="dd059e97ad7cf8b48a90d697d65331c0" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":30740405,"asset_id":2788008,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/30740405/download_file?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="2788008"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2788008"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2788008; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2788008]").text(description); $(".js-view-count[data-work-id=2788008]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2788008; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2788008']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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: "dd059e97ad7cf8b48a90d697d65331c0" } } $('.js-work-strip[data-work-id=2788008]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2788008,"title":"Improving the effectiveness of information retrieval with local context analysis","internal_url":"https://www.academia.edu/2788008/Improving_the_effectiveness_of_information_retrieval_with_local_context_analysis","owner_id":1389,"coauthors_can_edit":true,"owner":{"id":1389,"first_name":"W. 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Indri is an efficient, effective distributed search engine. Like INQUERY, it is based on the inference network framework and supports structured queries, but unlike INQUERY, it uses language modeling probabilities within the network which allows for added flexibility.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="0b7766a5b45c80408baf1c3763f60fe6" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":30740406,"asset_id":2788007,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/30740406/download_file?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="2788007"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2788007"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2788007; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2788007]").text(description); $(".js-view-count[data-work-id=2788007]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2788007; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2788007']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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: "0b7766a5b45c80408baf1c3763f60fe6" } } $('.js-work-strip[data-work-id=2788007]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2788007,"title":"Indri at TREC 2004: Terabyte track","internal_url":"https://www.academia.edu/2788007/Indri_at_TREC_2004_Terabyte_track","owner_id":1389,"coauthors_can_edit":true,"owner":{"id":1389,"first_name":"W. 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Generally speaking, statistical language modeling, or more simply, language modeling (LM), refers to the task of estimating a probability distribution that captures statistical regularities of natural language use.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="b4d9de387e5a523e793afa47d7ecdd82" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":30740408,"asset_id":2788006,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/30740408/download_file?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="2788006"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2788006"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2788006; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2788006]").text(description); $(".js-view-count[data-work-id=2788006]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2788006; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2788006']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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: "b4d9de387e5a523e793afa47d7ecdd82" } } $('.js-work-strip[data-work-id=2788006]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2788006,"title":"Statistical language modeling for information retrieval","internal_url":"https://www.academia.edu/2788006/Statistical_language_modeling_for_information_retrieval","owner_id":1389,"coauthors_can_edit":true,"owner":{"id":1389,"first_name":"W. 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Volume 6. Building an Intelligent Assistant: The Acquisition, Integration, and Maintenance of Complex Distributed Tasks" 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/2788025/Northeast_Artificial_Intelligence_Consortium_NAIC_Volume_6_Building_an_Intelligent_Assistant_The_Acquisition_Integration_and_Maintenance_of_Complex_Distributed_Tasks">Northeast Artificial Intelligence Consortium (NAIC). Volume 6. Building an Intelligent Assistant: The Acquisition, Integration, and Maintenance of Complex Distributed Tasks</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract: The Northeast Artificial Intelligence Consortium (NAIC) was created by the Air Force Sy...</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">Abstract: The Northeast Artificial Intelligence Consortium (NAIC) was created by the Air Force Systems Command, Rome Air Development Center, and the Office of Scientific Research. Its purpose was to conduct pertinent research in artificial intelligence and to perform activities ancillary to this research. This report describes progress during the existence of the NAIC on the technical research tasks undertaken at the member universities.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e751ee06b564551b1ccdb73c9eac0f39" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":30740565,"asset_id":2788025,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/30740565/download_file?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="2788025"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2788025"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2788025; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2788025]").text(description); $(".js-view-count[data-work-id=2788025]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2788025; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2788025']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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: "e751ee06b564551b1ccdb73c9eac0f39" } } $('.js-work-strip[data-work-id=2788025]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2788025,"title":"Northeast Artificial Intelligence Consortium (NAIC). Volume 6. Building an Intelligent Assistant: The Acquisition, Integration, and Maintenance of Complex Distributed Tasks","internal_url":"https://www.academia.edu/2788025/Northeast_Artificial_Intelligence_Consortium_NAIC_Volume_6_Building_an_Intelligent_Assistant_The_Acquisition_Integration_and_Maintenance_of_Complex_Distributed_Tasks","owner_id":1389,"coauthors_can_edit":true,"owner":{"id":1389,"first_name":"W. Bruce","middle_initials":null,"last_name":"Croft","page_name":"WBruceCroft","domain_name":"umass","created_at":"2008-08-13T11:28:23.544-07:00","display_name":"W. Bruce Croft","url":"https://umass.academia.edu/WBruceCroft"},"attachments":[{"id":30740565,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://a.academia-assets.com/images/blank-paper.jpg","file_name":"GetTRDoc.pdf","download_url":"https://www.academia.edu/attachments/30740565/download_file","bulk_download_file_name":"Northeast_Artificial_Intelligence_Consor.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/30740565/GetTRDoc-libre.pdf?1392107392=\u0026response-content-disposition=attachment%3B+filename%3DNortheast_Artificial_Intelligence_Consor.pdf\u0026Expires=1740055929\u0026Signature=JS3DJ9tu6ejKt62QWYuS1Gv-X1Qg-AFDYb2It0ie0XhY9wN5AFA3SgPW~R2PHxYcR2IVJQ6NmKJlGZoMY5O6akGPlTFEVV977evF0-RmJNQA2JoiKSY7YA3~UpHlEDVk1t-8wVvjUks1YT0RDD~RCm-8xACtqWmzch03UmTwjijFJ1~MmI0PmKpp5kjHDKj~R27pkBr1SkFjIslLYHzfJWFbjW90Wam16Z7hULhkKIk69yF9KaoqLXQGiJi4aH4e4aeKcXsQKYzBHKwwBU~ilRird-WAtZpxIxmzyvhYk9WusK3Qs6jy2LkC~s6HkbnqkNjXMXFjmLatmnw2vZLGpA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, 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="2788024"><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/2788024/Corpus_Based_Stemming_using_Co_occurrence_of_Word"><img alt="Research paper thumbnail of Corpus-Based Stemming using Co-occurrence of Word" 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/2788024/Corpus_Based_Stemming_using_Co_occurrence_of_Word">Corpus-Based Stemming using Co-occurrence of Word</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Stemming is used in many information retrieval (IR) systems to reduce variant word forms to commo...</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">Stemming is used in many information retrieval (IR) systems to reduce variant word forms to common roots. It is one of the simplest applications of natural language processing to IR, and one of the most e ective in terms of user acceptance and consistent, though small, retrieval improvements. Current stemming techniques do not, however, re ect the language use in speci c corpora and this can lead to occasional serious retrieval failures. We propose a technique for using corpus-based word variant co-occurrence statistics to modify or create a stemmer. The experimental results generated using English newspaper and legal text and Spanish text demonstrate the viability of this technique and its advantages relative to conventional approaches.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="32e79e5d0f348e914eb83163456dd95f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":30740561,"asset_id":2788024,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/30740561/download_file?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="2788024"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2788024"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2788024; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2788024]").text(description); $(".js-view-count[data-work-id=2788024]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2788024; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2788024']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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: "32e79e5d0f348e914eb83163456dd95f" } } $('.js-work-strip[data-work-id=2788024]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2788024,"title":"Corpus-Based Stemming using Co-occurrence of Word","internal_url":"https://www.academia.edu/2788024/Corpus_Based_Stemming_using_Co_occurrence_of_Word","owner_id":1389,"coauthors_can_edit":true,"owner":{"id":1389,"first_name":"W. Bruce","middle_initials":null,"last_name":"Croft","page_name":"WBruceCroft","domain_name":"umass","created_at":"2008-08-13T11:28:23.544-07:00","display_name":"W. Bruce Croft","url":"https://umass.academia.edu/WBruceCroft"},"attachments":[{"id":30740561,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://a.academia-assets.com/images/blank-paper.jpg","file_name":"10.1.1.35.604.pdf","download_url":"https://www.academia.edu/attachments/30740561/download_file","bulk_download_file_name":"Corpus_Based_Stemming_using_Co_occurrenc.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/30740561/10.1.1.35.604-libre.pdf?1392055682=\u0026response-content-disposition=attachment%3B+filename%3DCorpus_Based_Stemming_using_Co_occurrenc.pdf\u0026Expires=1740055929\u0026Signature=SdCJjbvMVQ5o~SO647pe4nqfKqfDb0VosjGmDeKmVwNZCsMDUCmpCBPifksKsNmXT4DzBPN19Z2GKzzh4rZZfUAOz82ozVuODrw5hg3Q9cT0z3EVO96xch~Fw9AqVudMu~oI2eGNityeppLIZv5bG7qBYDxZL8j-3WQNTcz0dEY-cP3VisHTR39IQaP2InGFCbydLVsyzGVaHTU5xgY7Lupawc4hloUglI8TfzuHvhuFwGrH0sAP9T2cn3Urlav~0JZvnwsvFSi7x4TjEEnDHXHnTpGGR3Hljo6rFJWtc~GRcg3Qc66oIe3kQORQ8kDDdSpcOJocfSHghLSlJEogsQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, 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="2788023"><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/2788023/Hierarchical_language_models_for_expert_finding_in_enterprise_corpora"><img alt="Research paper thumbnail of Hierarchical language models for expert finding in enterprise corpora" 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/2788023/Hierarchical_language_models_for_expert_finding_in_enterprise_corpora">Hierarchical language models for expert finding in enterprise corpora</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Enterprise corpora contain evidence of what employees work on and therefore can be used to automa...</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">Enterprise corpora contain evidence of what employees work on and therefore can be used to automatically find experts on a given topic. We present a general approach for representing the knowledge of a potential expert as a mixture of language models from associated documents. First we retrieve documents given the expert's name using a generative probabilistic technique and weight the retrieved documents according to expert-specific posterior distribution.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="7a01a546d7870904300310c78f813f6b" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":30838833,"asset_id":2788023,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/30838833/download_file?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="2788023"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2788023"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2788023; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2788023]").text(description); $(".js-view-count[data-work-id=2788023]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2788023; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2788023']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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: "7a01a546d7870904300310c78f813f6b" } } $('.js-work-strip[data-work-id=2788023]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2788023,"title":"Hierarchical language models for expert finding in enterprise corpora","internal_url":"https://www.academia.edu/2788023/Hierarchical_language_models_for_expert_finding_in_enterprise_corpora","owner_id":1389,"coauthors_can_edit":true,"owner":{"id":1389,"first_name":"W. Bruce","middle_initials":null,"last_name":"Croft","page_name":"WBruceCroft","domain_name":"umass","created_at":"2008-08-13T11:28:23.544-07:00","display_name":"W. Bruce Croft","url":"https://umass.academia.edu/WBruceCroft"},"attachments":[{"id":30838833,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://a.academia-assets.com/images/blank-paper.jpg","file_name":"hierarchical_language_models_for_expert_finding_in_enterprise_corpora.pdf","download_url":"https://www.academia.edu/attachments/30838833/download_file","bulk_download_file_name":"Hierarchical_language_models_for_expert.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/30838833/hierarchical_language_models_for_expert_finding_in_enterprise_corpora-libre.pdf?1393888348=\u0026response-content-disposition=attachment%3B+filename%3DHierarchical_language_models_for_expert.pdf\u0026Expires=1740055929\u0026Signature=gx6bM6RU45R-q7sl0PAJ0Bnuf3Y3cgQtr4jMzxFArU8o4TF3bH3oRZ9XwMUGETT1kh7uujE50zkidPtBPiJjduKrovemUOAVje5gnc7PR55vE-6G9rn~GvzOgsaLJ9QrlNRdK~6ASqVEHb-zPZDUUWkAad6v6dPVzrswUSpi06vpc1gc1gTWy6cCEBNNQ9R~BIr~XlcV4JREVCegNz7YGIAY~eQqc8oPRdlGzwfjByD74jJ7uMzWCa3diJsbFQwmyU07IEAyfuNRjv2sCdHg~WH4sCctAxm-S44LGo0QWGFgTSeKy~bJx3JvFCglcfPqISyLhk-DSLryWYncqzhwNQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, 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="2788022"><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/2788022/Beyond_bags_of_words_Modeling_implicit_user_preferences_in_information_retrieval"><img alt="Research paper thumbnail of Beyond bags of words: Modeling implicit user preferences in information retrieval" 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/2788022/Beyond_bags_of_words_Modeling_implicit_user_preferences_in_information_retrieval">Beyond bags of words: Modeling implicit user preferences in information retrieval</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract This paper reports on recent work in the field of information retrieval that attempts to...</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">Abstract This paper reports on recent work in the field of information retrieval that attempts to go beyond the overly simplified approach of representing documents and queries as bags of words. Simple models make it difficult to accurately model a user's information need. The model presented in the paper is based on Markov random fields and allows almost arbitrary features to be encoded. This provides a powerful mechanism for modeling many of the implicit constraints a user has in mind when formulating a query.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="629890029babb5f4c5a66148a87a4f65" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":30838829,"asset_id":2788022,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/30838829/download_file?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="2788022"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2788022"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2788022; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2788022]").text(description); $(".js-view-count[data-work-id=2788022]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2788022; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2788022']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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: "629890029babb5f4c5a66148a87a4f65" } } $('.js-work-strip[data-work-id=2788022]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2788022,"title":"Beyond bags of words: Modeling implicit user preferences in information retrieval","internal_url":"https://www.academia.edu/2788022/Beyond_bags_of_words_Modeling_implicit_user_preferences_in_information_retrieval","owner_id":1389,"coauthors_can_edit":true,"owner":{"id":1389,"first_name":"W. 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Compared to system memory, disk bandwidth is poor, and seek times are worse. We circumvent this problem by considering query evaluation strategies in main memory. We show how new accumulator trimming techniques combined with inverted list skipping can produce extremely high performance retrieval systems without resorting to methods that may harm effectiveness.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="07969388f8a1e2899b91d2cdaec797e4" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":30740554,"asset_id":2788021,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/30740554/download_file?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="2788021"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2788021"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2788021; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2788021]").text(description); $(".js-view-count[data-work-id=2788021]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2788021; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2788021']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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: "07969388f8a1e2899b91d2cdaec797e4" } } $('.js-work-strip[data-work-id=2788021]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2788021,"title":"Efficient document retrieval in main memory","internal_url":"https://www.academia.edu/2788021/Efficient_document_retrieval_in_main_memory","owner_id":1389,"coauthors_can_edit":true,"owner":{"id":1389,"first_name":"W. 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In particular, Boolean query operators are conveniently modeled as link matrices of the Bayesian Network. Prior work has shown, however, that these operators do not perform as well as the pnorm operators used for modeling query operators in the context of the vector space model. This motivates the search for alternative probabilistic formulations for these operators.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="975cf95bb6e07d1dc21521c198bcae31" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":31008972,"asset_id":2788020,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/31008972/download_file?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="2788020"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2788020"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2788020; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2788020]").text(description); $(".js-view-count[data-work-id=2788020]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2788020; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2788020']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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: "975cf95bb6e07d1dc21521c198bcae31" } } $('.js-work-strip[data-work-id=2788020]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2788020,"title":"PIC matrices: A computationally tractable class of probabilistic query operators","internal_url":"https://www.academia.edu/2788020/PIC_matrices_A_computationally_tractable_class_of_probabilistic_query_operators","owner_id":1389,"coauthors_can_edit":true,"owner":{"id":1389,"first_name":"W. Bruce","middle_initials":null,"last_name":"Croft","page_name":"WBruceCroft","domain_name":"umass","created_at":"2008-08-13T11:28:23.544-07:00","display_name":"W. Bruce Croft","url":"https://umass.academia.edu/WBruceCroft"},"attachments":[{"id":31008972,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/31008972/thumbnails/1.jpg","file_name":"pic_matrices_a_computationally_tractable_class_of_probabilistic_query_operators.pdf","download_url":"https://www.academia.edu/attachments/31008972/download_file","bulk_download_file_name":"PIC_matrices_A_computationally_tractable.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/31008972/pic_matrices_a_computationally_tractable_class_of_probabilistic_query_operators-libre.pdf?1392251201=\u0026response-content-disposition=attachment%3B+filename%3DPIC_matrices_A_computationally_tractable.pdf\u0026Expires=1740055929\u0026Signature=bkN5sozrd1dgYjvL9P7pOSySMbM8hNnecoIIQPiWFm4YpH6LabzDcUknWCj38tX4G9ZzX0rR6jua5AdYwicO~GHelyibsMHZzb2JIsRXtzzvf39VNDdIEGjz6Wu1RXxb~CQsxGxbCkeBqXaFvz7twaoAXlYtfEzZfj5GrEyPY2fy~Yw0JaYAvZjEJ7kO0cfkH8U9ykoJPkMG9H72NmCxfry3D3EjKpPmEyCPRLvO3iRr1Hg0FLbrAGejb-fzzbt4WYbuhkSfwW0C3j4YETN~PGAy-xfEtsievzh~LdXYFNINMvraZ3VvHmREoCk0kSlQzNrp5BrNU~adNvHEldFkWA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, 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="2788019"><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/2788019/Parameterized_concept_weighting_in_verbose_queries"><img alt="Research paper thumbnail of Parameterized concept weighting in verbose queries" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/2788019/Parameterized_concept_weighting_in_verbose_queries">Parameterized concept weighting in verbose queries</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract The majority of the current information retrieval models weight the query concepts (eg, ...</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">Abstract The majority of the current information retrieval models weight the query concepts (eg, terms or phrases) in an unsupervised manner, based solely on the collection statistics. In this paper, we go beyond the unsupervised estimation of concept weights, and propose a parameterized concept weighting model. In our model, the weight of each query concept is determined using a parameterized combination of diverse importance features.</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="2788019"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2788019"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2788019; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2788019]").text(description); $(".js-view-count[data-work-id=2788019]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2788019; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2788019']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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" } } $('.js-work-strip[data-work-id=2788019]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2788019,"title":"Parameterized concept weighting in verbose queries","internal_url":"https://www.academia.edu/2788019/Parameterized_concept_weighting_in_verbose_queries","owner_id":1389,"coauthors_can_edit":true,"owner":{"id":1389,"first_name":"W. Bruce","middle_initials":null,"last_name":"Croft","page_name":"WBruceCroft","domain_name":"umass","created_at":"2008-08-13T11:28:23.544-07:00","display_name":"W. Bruce Croft","url":"https://umass.academia.edu/WBruceCroft"},"attachments":[]}, 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="2788018"><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/2788018/Probabilistic_retrieval_of_ocr_degraded_text_using_n_grams"><img alt="Research paper thumbnail of Probabilistic retrieval of ocr degraded text using n-grams" 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/2788018/Probabilistic_retrieval_of_ocr_degraded_text_using_n_grams">Probabilistic retrieval of ocr degraded text using n-grams</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The retrieval of OCR degraded text using n-gram formulations within a probabilistic retrieval sys...</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 retrieval of OCR degraded text using n-gram formulations within a probabilistic retrieval system is examined in this paper. Direct retrieval of documents using n-gram databases of 2 and 3-grams or 2, 3, 4 and 5-grams resulted in improved retrieval performance over standard (word based) queries on the same data when a level of 10 percent degradation or worse was achieved.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="8f218f0c21e6b451b5391e3f67853974" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":30740569,"asset_id":2788018,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/30740569/download_file?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="2788018"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2788018"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2788018; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2788018]").text(description); $(".js-view-count[data-work-id=2788018]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2788018; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2788018']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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: "8f218f0c21e6b451b5391e3f67853974" } } $('.js-work-strip[data-work-id=2788018]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2788018,"title":"Probabilistic retrieval of ocr degraded text using n-grams","internal_url":"https://www.academia.edu/2788018/Probabilistic_retrieval_of_ocr_degraded_text_using_n_grams","owner_id":1389,"coauthors_can_edit":true,"owner":{"id":1389,"first_name":"W. 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Bruce Croft","url":"https://umass.academia.edu/WBruceCroft"},"attachments":[{"id":30740569,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://a.academia-assets.com/images/blank-paper.jpg","file_name":"Probabilistic_Retrieval_of_OCR_Degraded_Text.pdf","download_url":"https://www.academia.edu/attachments/30740569/download_file","bulk_download_file_name":"Probabilistic_retrieval_of_ocr_degraded.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/30740569/Probabilistic_Retrieval_of_OCR_Degraded_Text-libre.pdf?1392098466=\u0026response-content-disposition=attachment%3B+filename%3DProbabilistic_retrieval_of_ocr_degraded.pdf\u0026Expires=1740055929\u0026Signature=f0b7Ar8qdSzSF0t2nYcxF-JM5yCQxzb3LOEBEGbO17zXAgtVZrYj9z85i5ZC5lxtaNo94v3u48GsG7rMyO~l-3R~fsvfrZa~d6Yb9Lo8aHaWvw1k9RkzqOP5EdJnj4Zgb7KR3SwxiRZDhR4XtfLU4mcQVsGv~CgfMrvakkHj2~1X2z1A7Z35DkFqrP4yd2Ib86zd0OaaoQV76P6EYdKuptgDjoOILNWzPIa18HiU5mO9uPYbaQVOySgOXhwLBITQ0lg~INsCsC5RYprL3-6ECEi68kpdqHd3BB2Sk4O-MQVjxTFJwnG84Ts5fBA~nFD54S5cbhDd7K854MMdTyovvw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, 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="2788017"><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/2788017/Novelty_detection_based_on_sentence_level_patterns"><img alt="Research paper thumbnail of Novelty detection based on sentence level patterns" 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/2788017/Novelty_detection_based_on_sentence_level_patterns">Novelty detection based on sentence level patterns</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract The detection of new information in a document stream is an important component of many ...</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">Abstract The detection of new information in a document stream is an important component of many potential applications. In this paper, a new novelty detection approach based on the identification of sentence level patterns is proposed. Given a user's information need, some patterns in sentences such as combinations of query words, named entities and phrases, may contain more important and relevant information than single words.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="57d91c0544f5c760a24d76a623874afb" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":30740520,"asset_id":2788017,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/30740520/download_file?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="2788017"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2788017"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2788017; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2788017]").text(description); $(".js-view-count[data-work-id=2788017]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2788017; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2788017']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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: "57d91c0544f5c760a24d76a623874afb" } } $('.js-work-strip[data-work-id=2788017]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2788017,"title":"Novelty detection based on sentence level patterns","internal_url":"https://www.academia.edu/2788017/Novelty_detection_based_on_sentence_level_patterns","owner_id":1389,"coauthors_can_edit":true,"owner":{"id":1389,"first_name":"W. Bruce","middle_initials":null,"last_name":"Croft","page_name":"WBruceCroft","domain_name":"umass","created_at":"2008-08-13T11:28:23.544-07:00","display_name":"W. 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Bruce","middle_initials":null,"last_name":"Croft","page_name":"WBruceCroft","domain_name":"umass","created_at":"2008-08-13T11:28:23.544-07:00","display_name":"W. Bruce Croft","url":"https://umass.academia.edu/WBruceCroft"},"attachments":[]}, 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="2788015"><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/2788015/Evaluating_search_in_personal_social_media_collections"><img alt="Research paper thumbnail of Evaluating search in personal social media collections" 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/2788015/Evaluating_search_in_personal_social_media_collections">Evaluating search in personal social media collections</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract The prevalence of social media applications is generating potentially large personal arc...</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">Abstract The prevalence of social media applications is generating potentially large personal archives of posts, tweets, and other communications. The existence of these archives creates a need for search tools, which can be seen as an extension of current desktop search services. Little is currently known about the best search techniques for personal archives of social data, because of the difficulty of creating test collections.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="8173561b94c7107f93fecf467078afaf" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":30740489,"asset_id":2788015,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/30740489/download_file?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="2788015"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2788015"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2788015; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2788015]").text(description); $(".js-view-count[data-work-id=2788015]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2788015; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2788015']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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: "8173561b94c7107f93fecf467078afaf" } } $('.js-work-strip[data-work-id=2788015]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2788015,"title":"Evaluating search in personal social media collections","internal_url":"https://www.academia.edu/2788015/Evaluating_search_in_personal_social_media_collections","owner_id":1389,"coauthors_can_edit":true,"owner":{"id":1389,"first_name":"W. Bruce","middle_initials":null,"last_name":"Croft","page_name":"WBruceCroft","domain_name":"umass","created_at":"2008-08-13T11:28:23.544-07:00","display_name":"W. Bruce Croft","url":"https://umass.academia.edu/WBruceCroft"},"attachments":[{"id":30740489,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://a.academia-assets.com/images/blank-paper.jpg","file_name":"wsdm479-lee.pdf","download_url":"https://www.academia.edu/attachments/30740489/download_file","bulk_download_file_name":"Evaluating_search_in_personal_social_med.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/30740489/wsdm479-lee-libre.pdf?1392031947=\u0026response-content-disposition=attachment%3B+filename%3DEvaluating_search_in_personal_social_med.pdf\u0026Expires=1740055930\u0026Signature=FQsuoJpqZ8HEB~GAd9CPKFMxTuLX8ICDjCKekF1vmfkj8YZu0oXIpkxDb7w7rZjNMCQNbeaVW4OUPpMLdZHqisz6sBhUG8G6iXrWpB9bEzVQnuhQhIXvhEmpIUsyzvh3qyhSX6p81hMz6EqENtX~dkHHccFxi3TKRc1STdKlUOBzQI2gTdsniRTn1xvizeva8yeLiO3~NWrshjSjPuQfZSOC9AbJZen7tCSxYsn4kLXoo1dhv-9ZwUO~ZwfnLti32gx3BjZwYJx6N0nxRRLytOEjmEPWTFpOsLZrC9me3udUvQxEzdrXWoVkHgfLQdBuSJWSn-m1QyEMB9sI0QXobQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, 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="2788014"><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/2788014/The_History_of_Information_Retrieval_Research"><img alt="Research paper thumbnail of The History of Information Retrieval Research" 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/2788014/The_History_of_Information_Retrieval_Research">The History of Information Retrieval Research</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract This paper describes a brief history of the research and development of information retr...</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">Abstract This paper describes a brief history of the research and development of information retrieval systems starting with the creation of electromechanical searching devices, through to the early adoption of computers to search for items that are relevant to a user's query. The advances achieved by information retrieval researchers from the 1950s through to the present day are detailed next, focusing on the process of locating relevant information. The paper closes with speculation on where the future of information retrieval lies.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="99fb67474ff834ee2703deba2cc3d638" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":30740491,"asset_id":2788014,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/30740491/download_file?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="2788014"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2788014"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2788014; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2788014]").text(description); $(".js-view-count[data-work-id=2788014]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2788014; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2788014']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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: "99fb67474ff834ee2703deba2cc3d638" } } $('.js-work-strip[data-work-id=2788014]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2788014,"title":"The History of Information Retrieval Research","internal_url":"https://www.academia.edu/2788014/The_History_of_Information_Retrieval_Research","owner_id":1389,"coauthors_can_edit":true,"owner":{"id":1389,"first_name":"W. 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In particular, it...</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">Abstract The TIPSTER collection is unusual because of both its size and detail. In particular, it describes a set of information needs, as opposed to traditional queries. These detailed representations of information need are an opportunity for research on different methods of formulating queries. This paper describes several methods of constructing queries for the INQUERY information retrieval system, and then evaluates those methods on the TIPSTER document collection. Both AdHoc and Routing query processing methods are evaluated.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="49a4c58c23fbcdb5f17c9dba94d45952" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":30889753,"asset_id":2788013,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/30889753/download_file?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="2788013"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2788013"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2788013; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2788013]").text(description); $(".js-view-count[data-work-id=2788013]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2788013; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2788013']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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: "49a4c58c23fbcdb5f17c9dba94d45952" } } $('.js-work-strip[data-work-id=2788013]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2788013,"title":"An evaluation of query processing strategies using the TIPSTER collection","internal_url":"https://www.academia.edu/2788013/An_evaluation_of_query_processing_strategies_using_the_TIPSTER_collection","owner_id":1389,"coauthors_can_edit":true,"owner":{"id":1389,"first_name":"W. 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Bruce Croft","url":"https://umass.academia.edu/WBruceCroft"},"attachments":[{"id":30889753,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/30889753/thumbnails/1.jpg","file_name":"an_evaluation_of_query_processing_strategies_using_the_tipster_collection.pdf","download_url":"https://www.academia.edu/attachments/30889753/download_file","bulk_download_file_name":"An_evaluation_of_query_processing_strate.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/30889753/an_evaluation_of_query_processing_strategies_using_the_tipster_collection-libre.pdf?1392161369=\u0026response-content-disposition=attachment%3B+filename%3DAn_evaluation_of_query_processing_strate.pdf\u0026Expires=1740055930\u0026Signature=dPpd9y4PZo1gIN2x8l7dESin3Mgzx5CqZoSClMRBS1MarY2pObXL4EXSEpnPrEDqvjecrRLv7gk9KVhzBV0K2u-ZHvS3V65PZ-NVfsnc82lcK~4nfB85UipePTSi7EugY9OOT8SJkQkpn5rp1Ev3298zPaPBtPkS76N1jYW0NBFmT8FCFpMIsSzhoX1bTG~ATDJaeuFRKisbtrUbgpwvGP8j1EgAmLM2CFaK~HOD-grOutcLWI4PF1JetSy7T93qHfC3HYHSY8l3T6lQLIPgYGr10WEzBxGKMrXhnTd58H5rShZi27k0Ic80TcGsp~vlDmxj0~c9kE7MzX2BGxpO4w__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, 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="2788012"><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/2788012/Building_a_semantic_representation_for_personal_information"><img alt="Research paper thumbnail of Building a semantic representation for personal information" 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/2788012/Building_a_semantic_representation_for_personal_information">Building a semantic representation for personal information</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract A typical collection of personal information contains many documents and mentions many 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">Abstract A typical collection of personal information contains many documents and mentions many concepts (eg, person names, events, etc.). In this environment, associative browsing between these concepts and documents can be useful as a complement for search. Previous approaches in the area of semantic desktops aimed at addressing this task. However, they were not practical because they require tedious manual annotation by the user.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="23016aa3f37a3b9cb760be58412f8b0d" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":30740436,"asset_id":2788012,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/30740436/download_file?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="2788012"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2788012"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2788012; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2788012]").text(description); $(".js-view-count[data-work-id=2788012]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2788012; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2788012']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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: "23016aa3f37a3b9cb760be58412f8b0d" } } $('.js-work-strip[data-work-id=2788012]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2788012,"title":"Building a semantic representation for personal information","internal_url":"https://www.academia.edu/2788012/Building_a_semantic_representation_for_personal_information","owner_id":1389,"coauthors_can_edit":true,"owner":{"id":1389,"first_name":"W. Bruce","middle_initials":null,"last_name":"Croft","page_name":"WBruceCroft","domain_name":"umass","created_at":"2008-08-13T11:28:23.544-07:00","display_name":"W. 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Topic Detection and Tracking (TDT) is an example of such an application. In this paper we demonstrate that named entities serve as better choices of units for document representation over all words.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="34060e3340edc7cd4ce87419fcb2f211" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":30838824,"asset_id":2788011,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/30838824/download_file?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="2788011"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2788011"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2788011; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2788011]").text(description); $(".js-view-count[data-work-id=2788011]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2788011; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2788011']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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: "34060e3340edc7cd4ce87419fcb2f211" } } $('.js-work-strip[data-work-id=2788011]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2788011,"title":"Representing documents with named entities for story link detection (SLD)","internal_url":"https://www.academia.edu/2788011/Representing_documents_with_named_entities_for_story_link_detection_SLD_","owner_id":1389,"coauthors_can_edit":true,"owner":{"id":1389,"first_name":"W. 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Recently, an inference network based probabilistic retrieval model has been proposed, which views information retrieval as an evidential reasoning process in which multiple sources of evidence about document and query content are combined to estimate the relevance probabilities.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="0bb05b014ff7082e526302b789f7379f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":30740448,"asset_id":2788010,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/30740448/download_file?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="2788010"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2788010"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2788010; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2788010]").text(description); $(".js-view-count[data-work-id=2788010]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2788010; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2788010']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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: "0bb05b014ff7082e526302b789f7379f" } } $('.js-work-strip[data-work-id=2788010]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2788010,"title":"Combining automatic and manual index representations in probabilistic retrieval","internal_url":"https://www.academia.edu/2788010/Combining_automatic_and_manual_index_representations_in_probabilistic_retrieval","owner_id":1389,"coauthors_can_edit":true,"owner":{"id":1389,"first_name":"W. 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Bruce Croft","url":"https://umass.academia.edu/WBruceCroft"},"attachments":[{"id":30740448,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://a.academia-assets.com/images/blank-paper.jpg","file_name":"rajashekar95combining.pdf","download_url":"https://www.academia.edu/attachments/30740448/download_file","bulk_download_file_name":"Combining_automatic_and_manual_index_rep.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/30740448/rajashekar95combining-libre.pdf?1391834935=\u0026response-content-disposition=attachment%3B+filename%3DCombining_automatic_and_manual_index_rep.pdf\u0026Expires=1740055930\u0026Signature=GlCAkbDl8oGPO0hfXR8y7dwufLyoQkmI9h7zk0AF6~-5P6znsYDzPw2eT9NuE7vvbsjHLVphL5lcLDMqdlEYgsTWbjedtwIAcPmTuHYR~KfSCNV4~SANJAjMhFUE1qzbZvOym9lbQiY8U~rXu-TBPt6usJaF5vcv70qfLx1WG~nGVyfsAR~uCoTdP~1-O8635nlzrWfcR7w8fkPM6NVshPDmQ7n3tWAMRxMTk8WxHTu5AX8K5p~d5EthC-4Nm79N7TCcJN1ZU02LIO-FcpNzf0wX9Nd0oRu83sRXtNE3QNblly1EzP8RfXUg4jbOgTdgFygh8uySRHrme84kKpBJmQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, 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="2788009"><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/2788009/Automatic_boolean_query_suggestion_for_professional_search"><img alt="Research paper thumbnail of Automatic boolean query suggestion for professional search" 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/2788009/Automatic_boolean_query_suggestion_for_professional_search">Automatic boolean query suggestion for professional search</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract In professional search environments, such as patent search or legal search, search tasks...</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">Abstract In professional search environments, such as patent search or legal search, search tasks have unique characteristics: 1) users interactively issue several queries for a topic, and 2) users are willing to examine many retrieval results, ie, there is typically an emphasis on recall. Recent surveys have also verified that professional searchers continue to have a strong preference for Boolean queries because they provide a record of what documents were searched.</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="2788009"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2788009"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2788009; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2788009]").text(description); $(".js-view-count[data-work-id=2788009]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2788009; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2788009']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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" } } $('.js-work-strip[data-work-id=2788009]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2788009,"title":"Automatic boolean query suggestion for professional search","internal_url":"https://www.academia.edu/2788009/Automatic_boolean_query_suggestion_for_professional_search","owner_id":1389,"coauthors_can_edit":true,"owner":{"id":1389,"first_name":"W. Bruce","middle_initials":null,"last_name":"Croft","page_name":"WBruceCroft","domain_name":"umass","created_at":"2008-08-13T11:28:23.544-07:00","display_name":"W. Bruce Croft","url":"https://umass.academia.edu/WBruceCroft"},"attachments":[]}, 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="2788008"><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/2788008/Improving_the_effectiveness_of_information_retrieval_with_local_context_analysis"><img alt="Research paper thumbnail of Improving the effectiveness of information retrieval with local context analysis" 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/2788008/Improving_the_effectiveness_of_information_retrieval_with_local_context_analysis">Improving the effectiveness of information retrieval with local context analysis</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract Techniques for automatic query expansion have been extensively studied in information re...</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">Abstract Techniques for automatic query expansion have been extensively studied in information research as a means of addressing the word mismatch between queries and documents. These techniques can be categorized as either global or local. While global techniques rely on analysis of a whole collection to discover word relationships, local techniques emphasize analysis of the top-ranked documents retrieved for a query.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="dd059e97ad7cf8b48a90d697d65331c0" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":30740405,"asset_id":2788008,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/30740405/download_file?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="2788008"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2788008"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2788008; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2788008]").text(description); $(".js-view-count[data-work-id=2788008]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2788008; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2788008']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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: "dd059e97ad7cf8b48a90d697d65331c0" } } $('.js-work-strip[data-work-id=2788008]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2788008,"title":"Improving the effectiveness of information retrieval with local context analysis","internal_url":"https://www.academia.edu/2788008/Improving_the_effectiveness_of_information_retrieval_with_local_context_analysis","owner_id":1389,"coauthors_can_edit":true,"owner":{"id":1389,"first_name":"W. 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Indri is an efficient, effective distributed search engine. Like INQUERY, it is based on the inference network framework and supports structured queries, but unlike INQUERY, it uses language modeling probabilities within the network which allows for added flexibility.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="0b7766a5b45c80408baf1c3763f60fe6" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":30740406,"asset_id":2788007,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/30740406/download_file?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="2788007"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2788007"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2788007; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2788007]").text(description); $(".js-view-count[data-work-id=2788007]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2788007; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2788007']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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: "0b7766a5b45c80408baf1c3763f60fe6" } } $('.js-work-strip[data-work-id=2788007]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2788007,"title":"Indri at TREC 2004: Terabyte track","internal_url":"https://www.academia.edu/2788007/Indri_at_TREC_2004_Terabyte_track","owner_id":1389,"coauthors_can_edit":true,"owner":{"id":1389,"first_name":"W. Bruce","middle_initials":null,"last_name":"Croft","page_name":"WBruceCroft","domain_name":"umass","created_at":"2008-08-13T11:28:23.544-07:00","display_name":"W. 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Generally speaking, statistical language modeling, or more simply, language modeling (LM), refers to the task of estimating a probability distribution that captures statistical regularities of natural language use.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="b4d9de387e5a523e793afa47d7ecdd82" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":30740408,"asset_id":2788006,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/30740408/download_file?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="2788006"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="2788006"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 2788006; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=2788006]").text(description); $(".js-view-count[data-work-id=2788006]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 2788006; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='2788006']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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: "b4d9de387e5a523e793afa47d7ecdd82" } } $('.js-work-strip[data-work-id=2788006]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":2788006,"title":"Statistical language modeling for information retrieval","internal_url":"https://www.academia.edu/2788006/Statistical_language_modeling_for_information_retrieval","owner_id":1389,"coauthors_can_edit":true,"owner":{"id":1389,"first_name":"W. 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