{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/127272"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/127272","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Towards open-domain event understanding","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-03-28 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-03-28 without embargo terms","abstract_has_math":false,"creators":["Li, Sha"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Ji, Heng","Han, Jiawei","Zhai, Chengxiang","Callison-Burch, Chris"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-12-06","date_published":"2024-12-06","updated_at":"2026-07-22T22:25:03Z","subjects":["Event Extraction","Event Detection","Event Simulation","Few-shot Learning","Semi-supervised Learning"],"languages":["en","eng"],"rights":["Copyright 2024 Sha Li"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/127272","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Ji, Heng","Han, Jiawei","Zhai, Chengxiang","Callison-Burch, Chris"]},{"key":"dc:creator","label":"Author","values":["Li, Sha"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-12-06","2024-12"]},{"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":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"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":["Event Extraction","Event Detection","Event Simulation","Few-shot Learning","Semi-supervised Learning"]}]},{"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 2024 Sha Li"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/127272"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-03-28 without embargo terms","The student, Sha Li, accepted the attached license on 2024-12-05 at 13:34.","The student, Sha Li, submitted this Dissertation for approval on 2024-12-05 at 13:52.","This Dissertation was approved for publication on 2024-12-06 at 08:06.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21515 on 2025-03-28 at 14:28:21","Events are ubiquitous in our lives: from our everyday routines to yearly resolutions, from personal affairs to national emergencies, \"the only thing constant is change itself''. An event is essentially a change in the state of an object, described by an event type and its arguments. Conventionally, in the natural language processing space, events are grounded in event mentions that are on the word or phrase level. This is in fact, a pigeonholed view of events from three different aspects: (1) we only look at sentences without having the global context in mind; (2) we only care about events that are defined in the ontology; (3) we overlook relationships between event mentions. In this dissertation, we aim to improve the coverage, scope, and structure aspects of event extraction and understanding, by studying how to effectively and efficiently extract events, which events to extract, and how to obtain generalized event structure knowledge. Compared to prior work, we focus on enabling systems that are open-domain and can utilize wider context. Eventually, we showcase how multiple systems can collaborate to enable event forecasting and simulation."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Towards open-domain event understanding"]}]}],"canonical_facts":{"dc:contributor":["Ji, Heng","Han, Jiawei","Zhai, Chengxiang","Callison-Burch, Chris"],"dc:creator":["Li, Sha"],"dc:date":["2024-12-06","2024-12"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-03-28 without embargo terms","The student, Sha Li, accepted the attached license on 2024-12-05 at 13:34.","The student, Sha Li, submitted this Dissertation for approval on 2024-12-05 at 13:52.","This Dissertation was approved for publication on 2024-12-06 at 08:06.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21515 on 2025-03-28 at 14:28:21","Events are ubiquitous in our lives: from our everyday routines to yearly resolutions, from personal affairs to national emergencies, \"the only thing constant is change itself''. An event is essentially a change in the state of an object, described by an event type and its arguments. Conventionally, in the natural language processing space, events are grounded in event mentions that are on the word or phrase level. This is in fact, a pigeonholed view of events from three different aspects: (1) we only look at sentences without having the global context in mind; (2) we only care about events that are defined in the ontology; (3) we overlook relationships between event mentions. In this dissertation, we aim to improve the coverage, scope, and structure aspects of event extraction and understanding, by studying how to effectively and efficiently extract events, which events to extract, and how to obtain generalized event structure knowledge. Compared to prior work, we focus on enabling systems that are open-domain and can utilize wider context. Eventually, we showcase how multiple systems can collaborate to enable event forecasting and simulation."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/127272"],"dc:language":["en","eng"],"dc:rights":["Copyright 2024 Sha Li"],"dc:subject":["Event Extraction","Event Detection","Event Simulation","Few-shot Learning","Semi-supervised Learning"],"dc:title":["Towards open-domain event understanding"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:03Z"}