{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/120357"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/120357","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Ontology-guided annotation-free fine-grained entity typing","abstract":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-05-01","abstract_html":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-05-01","abstract_has_math":false,"creators":["Komarlu, Tanay Manjunath"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Han, Jiawei"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-05","date_published":"2023-05","updated_at":"2026-07-22T22:24:57Z","subjects":["Fine-grained Entity Typing","Annotation-free Entity Typing","Zero-shot Entity Typing","Information Extraction"],"languages":["en","eng"],"rights":["Copyright 2023 Tanay Komarlu"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/120357","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":["Komarlu, Tanay Manjunath"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2023-05","2023-04-12"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"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":["Fine-grained Entity Typing","Annotation-free Entity Typing","Zero-shot Entity Typing","Information Extraction"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2023 Tanay Komarlu"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/120357"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-05-01","The student, Tanay Komarlu, accepted the attached license on 2023-04-11 at 14:14.","The student, Tanay Komarlu, submitted this Thesis for approval on 2023-04-11 at 14:21.","This Thesis was approved for publication on 2023-04-12 at 16:35.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18953 on 2023-09-01 at 17:13:17","Fine-grained entity typing (FET), which assigns entities in text with context-sensitive, fine-grained semantic types, will play an important role in natural language understanding. FET has been studied extensively in natural language processing and typically relies on human-annotated corpora for training, which is costly and difficult to scale. Recent studies leverage pre-trained language models (PLMs) to generate rich and context-aware weak supervision for FET. However, the weak supervision based on masked language model (MLM) prompting still has two challenges: (1) a prompt often generates a set of tokens unsuitable for typing, and includes a mixture of rough and fine-grained types or (2) it does not incorporate the rich structural information in a given fine-grained type ontology. In this study, we vision that an ontology provides a semantic-rich, hierarchical structure, which will help select the best results generated by multiple PLM models and head words. Specifically, we propose a novel annotation-free, ontology-guided FET method, OntoType, which follows a type ontological structure, from coarse to fine, ensembles multiple PLM prompting results to generate a set of type candidates, and refines its type resolution, under the local context with a natural language inference model. Our experiments on the Ontonotes, FIGER, and NYT datasets using their associated ontological structures demonstrate that our method outperforms the state-of-the-art zero-shot fine-grained entity typing methods. Our error analysis shows that refinement of the existing ontology structures will further improve fine-grained entity typing."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Ontology-guided annotation-free fine-grained entity typing"]}]}],"canonical_facts":{"dc:contributor":["Han, Jiawei"],"dc:creator":["Komarlu, Tanay Manjunath"],"dc:date":["2023-05","2023-04-12"],"dc:description":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-05-01","The student, Tanay Komarlu, accepted the attached license on 2023-04-11 at 14:14.","The student, Tanay Komarlu, submitted this Thesis for approval on 2023-04-11 at 14:21.","This Thesis was approved for publication on 2023-04-12 at 16:35.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18953 on 2023-09-01 at 17:13:17","Fine-grained entity typing (FET), which assigns entities in text with context-sensitive, fine-grained semantic types, will play an important role in natural language understanding. FET has been studied extensively in natural language processing and typically relies on human-annotated corpora for training, which is costly and difficult to scale. Recent studies leverage pre-trained language models (PLMs) to generate rich and context-aware weak supervision for FET. However, the weak supervision based on masked language model (MLM) prompting still has two challenges: (1) a prompt often generates a set of tokens unsuitable for typing, and includes a mixture of rough and fine-grained types or (2) it does not incorporate the rich structural information in a given fine-grained type ontology. In this study, we vision that an ontology provides a semantic-rich, hierarchical structure, which will help select the best results generated by multiple PLM models and head words. Specifically, we propose a novel annotation-free, ontology-guided FET method, OntoType, which follows a type ontological structure, from coarse to fine, ensembles multiple PLM prompting results to generate a set of type candidates, and refines its type resolution, under the local context with a natural language inference model. Our experiments on the Ontonotes, FIGER, and NYT datasets using their associated ontological structures demonstrate that our method outperforms the state-of-the-art zero-shot fine-grained entity typing methods. Our error analysis shows that refinement of the existing ontology structures will further improve fine-grained entity typing."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/120357"],"dc:language":["en","eng"],"dc:rights":["Copyright 2023 Tanay Komarlu"],"dc:subject":["Fine-grained Entity Typing","Annotation-free Entity Typing","Zero-shot Entity Typing","Information Extraction"],"dc:title":["Ontology-guided annotation-free fine-grained entity typing"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:57Z"}