{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/99195"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/99195","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Extending Wikification: Nominal discovery, nominal linking, and the grounding of nouns","abstract":"Mention discovery, entity linking, and grounding are crucial steps in natural language understanding. Compared with named entities, the detection and linking of nominals are relatively little studied but essential since the grounding of nouns enriches information for humans that read documents. In this thesis, we address those problems by extending the Illinois Cross-lingual Wikifier with nominal linking and sense disambiguation. We train a nominal detector with the dictionary post-process to discover nominal mentions and classify them into predefined type categories. For the nominal linking, we propose a co-reference model that captures the pairwise features between the named entity and the nominal, and we integrate it with several linking heuristics. Finally, we ground nouns to their Wikipedia titles by adjusting the ranker of the Wikifier with extra features and the training on common nouns. Our proposed approaches show competitive performances on the benchmark datasets.","abstract_html":"Mention discovery, entity linking, and grounding are crucial steps in natural language understanding. Compared with named entities, the detection and linking of nominals are relatively little studied but essential since the grounding of nouns enriches information for humans that read documents. In this thesis, we address those problems by extending the Illinois Cross-lingual Wikifier with nominal linking and sense disambiguation. We train a nominal detector with the dictionary post-process to discover nominal mentions and classify them into predefined type categories. For the nominal linking, we propose a co-reference model that captures the pairwise features between the named entity and the nominal, and we integrate it with several linking heuristics. Finally, we ground nouns to their Wikipedia titles by adjusting the ranker of the Wikifier with extra features and the training on common nouns. Our proposed approaches show competitive performances on the benchmark datasets.","abstract_has_math":false,"creators":["Chen, Liang-Wei"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Roth, Dan"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-03-13T15:21:06Z","date_published":"2018-03-13T15:21:06Z","updated_at":"2026-07-22T22:24:37Z","subjects":["Wikification","Nominal entity recognition","Nominal entity disambiguation","Concept disambiguation","Natural language processing"],"languages":["en"],"rights":["Copyright 2017 Liang-Wei Chen"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/99195","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Roth, Dan"]},{"key":"dc:creator","label":"Author","values":["Chen, Liang-Wei"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2018-03-13T15:21:06Z","2020-03-14T09:15:22Z","2017-12-05","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":["Wikification","Nominal entity recognition","Nominal entity disambiguation","Concept disambiguation","Natural language processing"]}]},{"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 Liang-Wei Chen"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/99195"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Mention discovery, entity linking, and grounding are crucial steps in natural language understanding. Compared with named entities, the detection and linking of nominals are relatively little studied but essential since the grounding of nouns enriches information for humans that read documents. In this thesis, we address those problems by extending the Illinois Cross-lingual Wikifier with nominal linking and sense disambiguation. We train a nominal detector with the dictionary post-process to discover nominal mentions and classify them into predefined type categories. For the nominal linking, we propose a co-reference model that captures the pairwise features between the named entity and the nominal, and we integrate it with several linking heuristics. Finally, we ground nouns to their Wikipedia titles by adjusting the ranker of the Wikifier with extra features and the training on common nouns. Our proposed approaches show competitive performances on the benchmark datasets.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2019-12-01","The student, Liang-Wei Chen, accepted the attached license on 2017-12-04 at 22:36.","The student, Liang-Wei Chen, submitted this Thesis for approval on 2017-12-04 at 22:54.","This Thesis was approved for publication on 2017-12-05 at 15:57.","DSpace SAF Submission Ingestion Package generated from Vireo submission #11705 on 2018-03-13 at 09:55:38","Made available in DSpace on 2018-03-13T15:21:06Z (GMT). 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Compared with named entities, the detection and linking of nominals are relatively little studied but essential since the grounding of nouns enriches information for humans that read documents. In this thesis, we address those problems by extending the Illinois Cross-lingual Wikifier with nominal linking and sense disambiguation. We train a nominal detector with the dictionary post-process to discover nominal mentions and classify them into predefined type categories. For the nominal linking, we propose a co-reference model that captures the pairwise features between the named entity and the nominal, and we integrate it with several linking heuristics. Finally, we ground nouns to their Wikipedia titles by adjusting the ranker of the Wikifier with extra features and the training on common nouns. Our proposed approaches show competitive performances on the benchmark datasets.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2019-12-01","The student, Liang-Wei Chen, accepted the attached license on 2017-12-04 at 22:36.","The student, Liang-Wei Chen, submitted this Thesis for approval on 2017-12-04 at 22:54.","This Thesis was approved for publication on 2017-12-05 at 15:57.","DSpace SAF Submission Ingestion Package generated from Vireo submission #11705 on 2018-03-13 at 09:55:38","Made available in DSpace on 2018-03-13T15:21:06Z (GMT). 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