{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/99430"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/99430","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Hierarchical topic map generation for exploratory browsing","abstract":"This thesis proposes a novel model for automatically generate topic map for a document corpus with no supervision. We extend a previous approach to discovery of lexical relations from text data to construct a hierarchy of topics. Given a collection of documents, we will generate a set of topics on the fly which will help the user to efficiently navigate through the corpus space and finally land upon the desired document. We use Latent Dirichlet Allocation to generate the top level topics and then leverage paradigmatic and syntagmatic relations between words to construct the hierarchy. We characterize each topic in the hierarchy by a single phrase. Our topic map captures the requirements of user while he/she navigates through the corpus space. Instead of a rigid tree structure, we define links on topic map such that they take user to next desired finer level/related topic based on the history of already visited nodes in map/regions in the corpus. Experiments on DBLP titles datasets show that our topic map can be used very effectively and intuitively by the user to reach to the desired document.","abstract_html":"This thesis proposes a novel model for automatically generate topic map for a document corpus with no supervision. We extend a previous approach to discovery of lexical relations from text data to construct a hierarchy of topics. Given a collection of documents, we will generate a set of topics on the fly which will help the user to efficiently navigate through the corpus space and finally land upon the desired document. We use Latent Dirichlet Allocation to generate the top level topics and then leverage paradigmatic and syntagmatic relations between words to construct the hierarchy. We characterize each topic in the hierarchy by a single phrase. Our topic map captures the requirements of user while he/she navigates through the corpus space. Instead of a rigid tree structure, we define links on topic map such that they take user to next desired finer level/related topic based on the history of already visited nodes in map/regions in the corpus. Experiments on DBLP titles datasets show that our topic map can be used very effectively and intuitively by the user to reach to the desired document.","abstract_has_math":false,"creators":["Dai, Shengliang"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Zhai, ChengXiang"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-03-13T15:49:17Z","date_published":"2018-03-13T15:49:17Z","updated_at":"2026-07-22T22:24:37Z","subjects":["Hierarchical topic map","Exploratory browsing","Lexical relation"],"languages":["en"],"rights":["Copyright 2017 Shengliang Dai"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/99430","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Zhai, ChengXiang"]},{"key":"dc:creator","label":"Author","values":["Dai, Shengliang"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2018-03-13T15:49:17Z","2017-12-13","2017-12"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Hierarchical topic map","Exploratory browsing","Lexical relation"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2017 Shengliang Dai"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/99430"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["This thesis proposes a novel model for automatically generate topic map for a document corpus with no supervision. 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Experiments on DBLP titles datasets show that our topic map can be used very effectively and intuitively by the user to reach to the desired document.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2018-03-13 without embargo terms","The student, Shengliang Dai, accepted the attached license on 2017-12-13 at 14:28.","The student, Shengliang Dai, submitted this Thesis for approval on 2017-12-13 at 14:38.","This Thesis was approved for publication on 2017-12-13 at 15:22.","DSpace SAF Submission Ingestion Package generated from Vireo submission #11974 on 2018-03-13 at 10:12:25","Made available in DSpace on 2018-03-13T15:49:17Z (GMT). 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We use Latent Dirichlet Allocation to generate the top level topics and then leverage paradigmatic and syntagmatic relations between words to construct the hierarchy. We characterize each topic in the hierarchy by a single phrase. Our topic map captures the requirements of user while he/she navigates through the corpus space. Instead of a rigid tree structure, we define links on topic map such that they take user to next desired finer level/related topic based on the history of already visited nodes in map/regions in the corpus. Experiments on DBLP titles datasets show that our topic map can be used very effectively and intuitively by the user to reach to the desired document.","Submission original under an indefinite embargo labeled 'Open Access'. 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