{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/105012"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/105012","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"FUSE: Multi-faceted set expansion by coherent clustering of skip-grams","abstract":"\"Set expansion aims to expand a small set of seed entities into a complete set of relevant entities. Most existing approaches assume the input seed set is unambiguous and completely ignore the multi-faceted semantics of seed entities. As a result, given the seed set {\"\"Canon\"\", \"\"Sony\"\", \"\"Nikon\"\"}, previous methods return one mixed set of entities that are either camera brands or Japanese companies. In this thesis, we study the task of multi-faceted set expansion, which aims to capture all semantic facets in the seed set and returns multiple sets of entities, one for each semantic facet. We propose an unsupervised framework, FUSE, which consists of three major components: (1) facet discovery module: identifies all semantic facets of each seed entity by extracting and clustering its skip-grams, (2) facet fusion module: discovers shared semantic facets of the entire seed set by an optimization formulation, and (3) entity expansion module: expands each semantic facet by utilizing an iterative algorithm robust to skip-gram noise. Extensive experiments demonstrate that our algorithm, FUSE, can accurately identify multiple semantic facets of the seed set and generate quality entities for each facet.\"","abstract_html":""Set expansion aims to expand a small set of seed entities into a complete set of relevant entities. Most existing approaches assume the input seed set is unambiguous and completely ignore the multi-faceted semantics of seed entities. As a result, given the seed set {""Canon"", ""Sony"", ""Nikon""}, previous methods return one mixed set of entities that are either camera brands or Japanese companies. In this thesis, we study the task of multi-faceted set expansion, which aims to capture all semantic facets in the seed set and returns multiple sets of entities, one for each semantic facet. We propose an unsupervised framework, FUSE, which consists of three major components: (1) facet discovery module: identifies all semantic facets of each seed entity by extracting and clustering its skip-grams, (2) facet fusion module: discovers shared semantic facets of the entire seed set by an optimization formulation, and (3) entity expansion module: expands each semantic facet by utilizing an iterative algorithm robust to skip-gram noise. Extensive experiments demonstrate that our algorithm, FUSE, can accurately identify multiple semantic facets of the seed set and generate quality entities for each facet."","abstract_has_math":false,"creators":["Zhu, Wanzheng"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Han, Jiawei"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-08-23T20:35:51Z","date_published":"2019-08-23T20:35:51Z","updated_at":"2026-07-22T22:24:44Z","subjects":["Set Expansion, Word Sense Disambiguation"],"languages":["en"],"rights":["Copyright 2019 Wanzheng Zhu"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/105012","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Han, Jiawei"]},{"key":"dc:creator","label":"Author","values":["Zhu, Wanzheng"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2019-08-23T20:35:51Z","2021-08-24T09:15:16Z","2019-04-12","2019-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"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":["Set Expansion, Word Sense Disambiguation"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2019 Wanzheng Zhu"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/105012"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["\"Set expansion aims to expand a small set of seed entities into a complete set of relevant entities. Most existing approaches assume the input seed set is unambiguous and completely ignore the multi-faceted semantics of seed entities. As a result, given the seed set {\"\"Canon\"\", \"\"Sony\"\", \"\"Nikon\"\"}, previous methods return one mixed set of entities that are either camera brands or Japanese companies. In this thesis, we study the task of multi-faceted set expansion, which aims to capture all semantic facets in the seed set and returns multiple sets of entities, one for each semantic facet. We propose an unsupervised framework, FUSE, which consists of three major components: (1) facet discovery module: identifies all semantic facets of each seed entity by extracting and clustering its skip-grams, (2) facet fusion module: discovers shared semantic facets of the entire seed set by an optimization formulation, and (3) entity expansion module: expands each semantic facet by utilizing an iterative algorithm robust to skip-gram noise. Extensive experiments demonstrate that our algorithm, FUSE, can accurately identify multiple semantic facets of the seed set and generate quality entities for each facet.\"","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2021-05-01","The student, Wanzheng Zhu, accepted the attached license on 2019-04-12 at 12:45.","The student, Wanzheng Zhu, submitted this Thesis for approval on 2019-04-12 at 12:52.","This Thesis was approved for publication on 2019-04-12 at 15:21.","DSpace SAF Submission Ingestion Package generated from Vireo submission #13589 on 2019-08-22 at 15:05:58","Made available in DSpace on 2019-08-23T20:35:51Z (GMT). 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Most existing approaches assume the input seed set is unambiguous and completely ignore the multi-faceted semantics of seed entities. As a result, given the seed set {\"\"Canon\"\", \"\"Sony\"\", \"\"Nikon\"\"}, previous methods return one mixed set of entities that are either camera brands or Japanese companies. In this thesis, we study the task of multi-faceted set expansion, which aims to capture all semantic facets in the seed set and returns multiple sets of entities, one for each semantic facet. We propose an unsupervised framework, FUSE, which consists of three major components: (1) facet discovery module: identifies all semantic facets of each seed entity by extracting and clustering its skip-grams, (2) facet fusion module: discovers shared semantic facets of the entire seed set by an optimization formulation, and (3) entity expansion module: expands each semantic facet by utilizing an iterative algorithm robust to skip-gram noise. Extensive experiments demonstrate that our algorithm, FUSE, can accurately identify multiple semantic facets of the seed set and generate quality entities for each facet.\"","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2021-05-01","The student, Wanzheng Zhu, accepted the attached license on 2019-04-12 at 12:45.","The student, Wanzheng Zhu, submitted this Thesis for approval on 2019-04-12 at 12:52.","This Thesis was approved for publication on 2019-04-12 at 15:21.","DSpace SAF Submission Ingestion Package generated from Vireo submission #13589 on 2019-08-22 at 15:05:58","Made available in DSpace on 2019-08-23T20:35:51Z (GMT). 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