{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/102393"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/102393","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"A study of coherence in entity linking","abstract":"Entity linking (EL) is the task of mapping entities, such as persons, locations, organizations, etc., in text to a corresponding record in a knowledge base (KB) like Wikipedia or Freebase. In this paper we present, for the first time, a controlled study of one aspect of this problem called coherence. Further we show that many state-of-the-art models for EL reduce to the same basic architecture. Based on this general model we suggest that any system can theoretically bene t from using coherence although most do not. Our experimentation suggests that this is because the common approaches to measuring coherence among entities produce only weak signals. Therefore we argue that the way forward for research into coherence in EL is not by seeking new methods for performing inference but rather better methods for representing and comparing entities based off of existing structured data resources such as DBPedia and Wikidata.","abstract_html":"Entity linking (EL) is the task of mapping entities, such as persons, locations, organizations, etc., in text to a corresponding record in a knowledge base (KB) like Wikipedia or Freebase. In this paper we present, for the first time, a controlled study of one aspect of this problem called coherence. Further we show that many state-of-the-art models for EL reduce to the same basic architecture. Based on this general model we suggest that any system can theoretically bene t from using coherence although most do not. Our experimentation suggests that this is because the common approaches to measuring coherence among entities produce only weak signals. Therefore we argue that the way forward for research into coherence in EL is not by seeking new methods for performing inference but rather better methods for representing and comparing entities based off of existing structured data resources such as DBPedia and Wikidata.","abstract_has_math":false,"creators":["Duncan, Chase"],"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":2019,"date_issued":"2019-02-06T19:32:38Z","date_published":"2019-02-06T19:32:38Z","updated_at":"2026-07-22T22:24:40Z","subjects":["Entity Linking, Machine Learning, NLP"],"languages":["en"],"rights":["Copyright 2018 Chase Duncan"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/102393","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":["Duncan, Chase"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2019-02-06T19:32:38Z","2018-08-28","2018-12"]},{"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":["Entity Linking, Machine Learning, NLP"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2018 Chase Duncan"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/102393"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Entity linking (EL) is the task of mapping entities, such as persons, locations, organizations, etc., in text to a corresponding record in a knowledge base (KB) like Wikipedia or Freebase. In this paper we present, for the first time, a controlled study of one aspect of this problem called coherence. Further we show that many state-of-the-art models for EL reduce to the same basic architecture. Based on this general model we suggest that any system can theoretically bene t from using coherence although most do not. Our experimentation suggests that this is because the common approaches to measuring coherence among entities produce only weak signals. Therefore we argue that the way forward for research into coherence in EL is not by seeking new methods for performing inference but rather better methods for representing and comparing entities based off of existing structured data resources such as DBPedia and Wikidata.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2019-02-05 without embargo terms","The student, Chase Duncan, accepted the attached license on 2018-08-24 at 16:50.","The student, Chase Duncan, submitted this Thesis for approval on 2018-08-24 at 16:54.","This Thesis was approved for publication on 2018-08-28 at 11:44.","DSpace SAF Submission Ingestion Package generated from Vireo submission #12983 on 2019-02-05 at 11:07:40","Made available in DSpace on 2019-02-06T19:32:38Z (GMT). No. of bitstreams: 2 DUNCAN-THESIS-2018.pdf: 305860 bytes, checksum: 666eee4096800df2a1dcd0e7c6edf43f (MD5) LICENSE.txt: 4209 bytes, checksum: 16f282f4d9ad920b8e776d4e32bbb25c (MD5) Previous issue date: 2018-08-28"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["A study of coherence in entity linking"]}]}],"canonical_facts":{"dc:contributor":["Roth, Dan"],"dc:creator":["Duncan, Chase"],"dc:date":["2019-02-06T19:32:38Z","2018-08-28","2018-12"],"dc:description":["Entity linking (EL) is the task of mapping entities, such as persons, locations, organizations, etc., in text to a corresponding record in a knowledge base (KB) like Wikipedia or Freebase. In this paper we present, for the first time, a controlled study of one aspect of this problem called coherence. Further we show that many state-of-the-art models for EL reduce to the same basic architecture. Based on this general model we suggest that any system can theoretically bene t from using coherence although most do not. Our experimentation suggests that this is because the common approaches to measuring coherence among entities produce only weak signals. Therefore we argue that the way forward for research into coherence in EL is not by seeking new methods for performing inference but rather better methods for representing and comparing entities based off of existing structured data resources such as DBPedia and Wikidata.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2019-02-05 without embargo terms","The student, Chase Duncan, accepted the attached license on 2018-08-24 at 16:50.","The student, Chase Duncan, submitted this Thesis for approval on 2018-08-24 at 16:54.","This Thesis was approved for publication on 2018-08-28 at 11:44.","DSpace SAF Submission Ingestion Package generated from Vireo submission #12983 on 2019-02-05 at 11:07:40","Made available in DSpace on 2019-02-06T19:32:38Z (GMT). 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