{"id":{"repo_id":"unt","oai_identifier":"info:ark/67531/metadc4275"},"canonical_url":"https://search.dev.ndltd.org/etd/unt/info:ark/67531/metadc4275","repository":{"repo_id":"unt","name":"University of North Texas","base_url":"https://digital.library.unt.edu/oai/"},"display":{"title":"Building an Intelligent Filtering System Using Idea Indexing","abstract":"The widely used vector model maintains its popularity because of its simplicity, fast speed, and the appeal of using spatial proximity for semantic proximity. However, this model faces a disadvantage that is associated with the vagueness from keywords overlapping. Efforts have been made to improve the vector model. The research on improving document representation has been focused on four areas, namely, statistical co-occurrence of related items, forming term phrases, grouping of related words, and representing the content of documents. In this thesis, we propose the idea-indexing model to improve document representation for the filtering task in IR. The idea-indexing model matches document terms with the ideas they express and indexes the document with these ideas. This indexing scheme represents the document with its semantics instead of sets of independent terms. We show in this thesis that indexing with ideas leads to better performance.","abstract_html":"The widely used vector model maintains its popularity because of its simplicity, fast speed, and the appeal of using spatial proximity for semantic proximity. However, this model faces a disadvantage that is associated with the vagueness from keywords overlapping. Efforts have been made to improve the vector model. The research on improving document representation has been focused on four areas, namely, statistical co-occurrence of related items, forming term phrases, grouping of related words, and representing the content of documents. In this thesis, we propose the idea-indexing model to improve document representation for the filtering task in IR. The idea-indexing model matches document terms with the ideas they express and indexes the document with these ideas. This indexing scheme represents the document with its semantics instead of sets of independent terms. We show in this thesis that indexing with ideas leads to better performance.","abstract_has_math":false,"creators":["Yang, Li"],"institution":"University of North Texas","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Mihalcea, Rada, 1974-","Swigger, Kathleen M.","Brazile, Robert"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2003,"date_issued":"2003-08","date_published":"2003-08","updated_at":"2026-07-24T05:35:09Z","subjects":["Information retrieval.","Automatic indexing.","Intelligent Filtering System","vector model","term indexing","idea indexing","document representation"],"languages":["English"],"rights":["Public","Copyright","Yang, Li","Copyright is held by the author, unless otherwise noted. All rights reserved."],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["oclc: 53783233","https://digital.library.unt.edu/ark:/67531/metadc4275/","ark: ark:/67531/metadc4275"],"render_values":[{"text":"oclc: 53783233","href":null,"code":true},{"text":"https://digital.library.unt.edu/ark:/67531/metadc4275/","href":"https://digital.library.unt.edu/ark:/67531/metadc4275/","code":true},{"text":"ark: ark:/67531/metadc4275","href":null,"code":true}]}]},"links":{"outbound_url":"https://doi.org/10.12794/metadc4275","outbound_label":"DOI","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Mihalcea, Rada, 1974-","Swigger, Kathleen M.","Brazile, Robert"]},{"key":"dc:creator","label":"Author","values":["Yang, Li"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2003-08"]},{"key":"dc:publisher","label":"Institution","values":["University of North Texas"]},{"key":"dc:type","label":"Dc Type","values":["Thesis or Dissertation"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Information retrieval.","Automatic indexing.","Intelligent Filtering System","vector model","term indexing","idea indexing","document representation"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English"]},{"key":"dc:rights","label":"Dc Rights","values":["Public","Copyright","Yang, Li","Copyright is held by the author, unless otherwise noted. 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In this thesis, we propose the idea-indexing model to improve document representation for the filtering task in IR. The idea-indexing model matches document terms with the ideas they express and indexes the document with these ideas. This indexing scheme represents the document with its semantics instead of sets of independent terms. We show in this thesis that indexing with ideas leads to better performance."]},{"key":"dc:format","label":"Dc Format","values":["Text"]},{"key":"dc:title","label":"Title","values":["Building an Intelligent Filtering System Using Idea Indexing"]}]}],"canonical_facts":{"dc:contributor":["Mihalcea, Rada, 1974-","Swigger, Kathleen M.","Brazile, Robert"],"dc:creator":["Yang, Li"],"dc:date":["2003-08"],"dc:description":["The widely used vector model maintains its popularity because of its simplicity, fast speed, and the appeal of using spatial proximity for semantic proximity. However, this model faces a disadvantage that is associated with the vagueness from keywords overlapping. Efforts have been made to improve the vector model. The research on improving document representation has been focused on four areas, namely, statistical co-occurrence of related items, forming term phrases, grouping of related words, and representing the content of documents. In this thesis, we propose the idea-indexing model to improve document representation for the filtering task in IR. The idea-indexing model matches document terms with the ideas they express and indexes the document with these ideas. This indexing scheme represents the document with its semantics instead of sets of independent terms. 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