{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/101214"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/101214","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Fine-grained entity typing system - design and analysis","abstract":"Named entity recognition (NER) is a natural language processing (NLP) task that involves identifying mentions (spans of text) denoting entities in a given text document and assigning them a semantic category/type from a given taxonomy. It is considered to be one of the fundamental tasks in NLP and forms the basis for higher level understanding. In this thesis, we deal with fine-grained entity type recognition, which is a variant of the classic NER task where the usual types are sub-divided into fine-grained types. We show that the current approaches, which address this problem using only local context, are insufficient to completely address the problem. We systematically identify the fundamental challenges and misconceptions that underlie the assumptions, approaches and evaluation methodologies of this task and propose improvements and alternatives. We do this by first analyzing the role of context and background knowledge in the task of fine-grained entity typing. Second, we introduce a modular architecture for fine-grained typing of entities and show that a rather simple instantiation of these modules reaches the state-of-the-art performance.","abstract_html":"Named entity recognition (NER) is a natural language processing (NLP) task that involves identifying mentions (spans of text) denoting entities in a given text document and assigning them a semantic category/type from a given taxonomy. It is considered to be one of the fundamental tasks in NLP and forms the basis for higher level understanding. In this thesis, we deal with fine-grained entity type recognition, which is a variant of the classic NER task where the usual types are sub-divided into fine-grained types. We show that the current approaches, which address this problem using only local context, are insufficient to completely address the problem. We systematically identify the fundamental challenges and misconceptions that underlie the assumptions, approaches and evaluation methodologies of this task and propose improvements and alternatives. We do this by first analyzing the role of context and background knowledge in the task of fine-grained entity typing. Second, we introduce a modular architecture for fine-grained typing of entities and show that a rather simple instantiation of these modules reaches the state-of-the-art performance.","abstract_has_math":false,"creators":["Muddireddy, Pavankumar Reddy"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical and Computer Engineering","degree_department":null,"school":null,"contributors":["Roth, Dan"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-09-04T20:36:52Z","date_published":"2018-09-04T20:36:52Z","updated_at":"2026-07-22T22:24:38Z","subjects":["named entity recognition","NER","fine-grained named entity recognition","finet","figer","information retrieval","entity typing","fine-grained typing"],"languages":["en"],"rights":["Copyright 2018 Pavankumar Reddy Muddireddy"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/101214","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":["Muddireddy, Pavankumar Reddy"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2018-09-04T20:36:52Z","2020-09-05T09:15:29Z","2018-04-24","2018-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical and Computer Engineering"]},{"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":["named entity recognition","NER","fine-grained named entity recognition","finet","figer","information retrieval","entity typing","fine-grained typing"]}]},{"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 Pavankumar Reddy Muddireddy"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/101214"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Named entity recognition (NER) is a natural language processing (NLP) task that involves identifying mentions (spans of text) denoting entities in a given text document and assigning them a semantic category/type from a given taxonomy. It is considered to be one of the fundamental tasks in NLP and forms the basis for higher level understanding. In this thesis, we deal with fine-grained entity type recognition, which is a variant of the classic NER task where the usual types are sub-divided into fine-grained types. We show that the current approaches, which address this problem using only local context, are insufficient to completely address the problem. We systematically identify the fundamental challenges and misconceptions that underlie the assumptions, approaches and evaluation methodologies of this task and propose improvements and alternatives. We do this by first analyzing the role of context and background knowledge in the task of fine-grained entity typing. Second, we introduce a modular architecture for fine-grained typing of entities and show that a rather simple instantiation of these modules reaches the state-of-the-art performance.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2020-05-01","The student, Pavankumar Reddy Muddireddy, accepted the attached license on 2018-04-23 at 17:30.","The student, Pavankumar Reddy Muddireddy, submitted this Thesis for approval on 2018-04-23 at 17:42.","This Thesis was approved for publication on 2018-04-24 at 09:20.","DSpace SAF Submission Ingestion Package generated from Vireo submission #12436 on 2018-08-31 at 17:21:20","Made available in DSpace on 2018-09-04T20:36:52Z (GMT). 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It is considered to be one of the fundamental tasks in NLP and forms the basis for higher level understanding. In this thesis, we deal with fine-grained entity type recognition, which is a variant of the classic NER task where the usual types are sub-divided into fine-grained types. We show that the current approaches, which address this problem using only local context, are insufficient to completely address the problem. We systematically identify the fundamental challenges and misconceptions that underlie the assumptions, approaches and evaluation methodologies of this task and propose improvements and alternatives. We do this by first analyzing the role of context and background knowledge in the task of fine-grained entity typing. Second, we introduce a modular architecture for fine-grained typing of entities and show that a rather simple instantiation of these modules reaches the state-of-the-art performance.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2020-05-01","The student, Pavankumar Reddy Muddireddy, accepted the attached license on 2018-04-23 at 17:30.","The student, Pavankumar Reddy Muddireddy, submitted this Thesis for approval on 2018-04-23 at 17:42.","This Thesis was approved for publication on 2018-04-24 at 09:20.","DSpace SAF Submission Ingestion Package generated from Vireo submission #12436 on 2018-08-31 at 17:21:20","Made available in DSpace on 2018-09-04T20:36:52Z (GMT). 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