{"id":{"repo_id":"unt","oai_identifier":"info:ark/67531/metadc2456"},"canonical_url":"https://search.dev.ndltd.org/etd/unt/info:ark/67531/metadc2456","repository":{"repo_id":"unt","name":"University of North Texas","base_url":"https://digital.library.unt.edu/oai/"},"display":{"title":"A Study of Graphically Chosen Features for Representation of TREC Topic-Document Sets","abstract":"Document representation is important for computer-based text processing. Good document representations must include at least the most salient concepts of the document. Documents exist in a multidimensional space that difficult the identification of what concepts to include. A current problem is to measure the effectiveness of the different strategies that have been proposed to accomplish this task. As a contribution towards this goal, this dissertation studied the visual inter-document relationship in a dimensionally reduced space. The same treatment was done on full text and on three document representations. Two of the representations were based on the assumption that the salient features in a document set follow the chi-distribution in the whole document set. The third document representation identified features through a novel method. A Coefficient of Variability was calculated by normalizing the Cartesian distance of the discriminating value in the relevant and the non-relevant document subsets. Also, the local dictionary method was used. Cosine similarity values measured the inter-document distance in the information space and formed a matrix to serve as input to the Multi-Dimensional Scale (MDS) procedure. A Precision-Recall procedure was averaged across all treatments to statistically compare them. Treatments were not found to be statistically the same and the null hypotheses were rejected.","abstract_html":"Document representation is important for computer-based text processing. Good document representations must include at least the most salient concepts of the document. Documents exist in a multidimensional space that difficult the identification of what concepts to include. A current problem is to measure the effectiveness of the different strategies that have been proposed to accomplish this task. As a contribution towards this goal, this dissertation studied the visual inter-document relationship in a dimensionally reduced space. The same treatment was done on full text and on three document representations. Two of the representations were based on the assumption that the salient features in a document set follow the chi-distribution in the whole document set. The third document representation identified features through a novel method. A Coefficient of Variability was calculated by normalizing the Cartesian distance of the discriminating value in the relevant and the non-relevant document subsets. Also, the local dictionary method was used. Cosine similarity values measured the inter-document distance in the information space and formed a matrix to serve as input to the Multi-Dimensional Scale (MDS) procedure. A Precision-Recall procedure was averaged across all treatments to statistically compare them. Treatments were not found to be statistically the same and the null hypotheses were rejected.","abstract_has_math":false,"creators":["Oyarce, Guillermo Alfredo"],"institution":"University of North Texas","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Rorvig, Mark E.","Young, Jon I.","Turner, Philip M., 1948-","Totten, Herman L."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2000,"date_issued":"2000-05","date_published":"2000-05","updated_at":"2026-07-24T05:34:52Z","subjects":["Information storage and retrieval systems.","Text processing (Computer science)","chi-distribution","Coefficient of Variability","document representation","full-text document"],"languages":["English"],"rights":["Use restricted to UNT Community","Copyright","Oyarce, Guillermo Alfredo","Copyright is held by the author, unless otherwise noted. All rights reserved."],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["oclc: 47203873","untcat: b2301004","https://digital.library.unt.edu/ark:/67531/metadc2456/","ark: ark:/67531/metadc2456"],"render_values":[{"text":"oclc: 47203873","href":null,"code":true},{"text":"untcat: b2301004","href":null,"code":true},{"text":"https://digital.library.unt.edu/ark:/67531/metadc2456/","href":"https://digital.library.unt.edu/ark:/67531/metadc2456/","code":true},{"text":"ark: ark:/67531/metadc2456","href":null,"code":true}]}]},"links":{"outbound_url":"https://doi.org/10.12794/metadc2456","outbound_label":"DOI","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Rorvig, Mark E.","Young, Jon I.","Turner, Philip M., 1948-","Totten, Herman L."]},{"key":"dc:creator","label":"Author","values":["Oyarce, Guillermo Alfredo"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2000-05"]},{"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 storage and retrieval systems.","Text processing (Computer science)","chi-distribution","Coefficient of Variability","document representation","full-text document"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English"]},{"key":"dc:rights","label":"Dc Rights","values":["Use restricted to UNT Community","Copyright","Oyarce, Guillermo Alfredo","Copyright is held by the author, unless otherwise noted. 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The same treatment was done on full text and on three document representations. Two of the representations were based on the assumption that the salient features in a document set follow the chi-distribution in the whole document set. The third document representation identified features through a novel method. A Coefficient of Variability was calculated by normalizing the Cartesian distance of the discriminating value in the relevant and the non-relevant document subsets. Also, the local dictionary method was used. Cosine similarity values measured the inter-document distance in the information space and formed a matrix to serve as input to the Multi-Dimensional Scale (MDS) procedure. A Precision-Recall procedure was averaged across all treatments to statistically compare them. Treatments were not found to be statistically the same and the null hypotheses were rejected."]},{"key":"dc:format","label":"Dc Format","values":["Text"]},{"key":"dc:title","label":"Title","values":["A Study of Graphically Chosen Features for Representation of TREC Topic-Document Sets"]}]}],"canonical_facts":{"dc:contributor":["Rorvig, Mark E.","Young, Jon I.","Turner, Philip M., 1948-","Totten, Herman L."],"dc:creator":["Oyarce, Guillermo Alfredo"],"dc:date":["2000-05"],"dc:description":["Document representation is important for computer-based text processing. Good document representations must include at least the most salient concepts of the document. Documents exist in a multidimensional space that difficult the identification of what concepts to include. A current problem is to measure the effectiveness of the different strategies that have been proposed to accomplish this task. 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