{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/114027"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/114027","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Event network embedding","abstract":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2023-12-01","abstract_html":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2023-12-01","abstract_has_math":false,"creators":["Zeng, Qi"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Ji, Heng"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-04-29T21:47:48Z","date_published":"2022-04-29T21:47:48Z","updated_at":"2026-07-22T22:24:54Z","subjects":["Computer science"],"languages":["en","eng"],"rights":["Copyright 2021 Qi Zeng"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/114027","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Ji, Heng"]},{"key":"dc:creator","label":"Author","values":["Zeng, Qi"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-04-29T21:47:48Z","2024-04-29T21:47:53Z","2021-12","2021-12-09"]},{"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":["Computer science"]}]},{"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 2021 Qi Zeng"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/114027"]}]},{"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 2023-12-01","The student, Qi Zeng, accepted the attached license on 2021-12-09 at 10:50.","The student, Qi Zeng, submitted this Thesis for approval on 2021-12-09 at 10:57.","This Thesis was approved for publication on 2021-12-09 at 13:26.","DSpace SAF Submission Ingestion Package generated from Vireo submission #17432 on 2022-04-06 at 17:18:05","Made available in DSpace on 2022-04-29T21:47:48Z (GMT). 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The graph encoder is trained by minimizing both structural and semantic losses. We develop a new series of structured probing tasks, and show that our approach effectively outperforms baseline models on node typing, argument role classification, and event coreference resolution. As a direct application, We introduce a new task, Unsupervised Event Schema Graph Matching, which aims to align event instance graphs and event schema graphs by finding node correspondence. We develop the first benchmark and collect a dataset of 3,740 event instance graphs from IED-scenario news articles, 75 of which are paired with 4 event schema graphs by human annotation. Our analysis on this task sheds light on the shortcomings of current state-of-the-art models on this event understanding task."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Event network embedding"]}]}],"canonical_facts":{"dc:contributor":["Ji, Heng"],"dc:creator":["Zeng, Qi"],"dc:date":["2022-04-29T21:47:48Z","2024-04-29T21:47:53Z","2021-12","2021-12-09"],"dc:description":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2023-12-01","The student, Qi Zeng, accepted the attached license on 2021-12-09 at 10:50.","The student, Qi Zeng, submitted this Thesis for approval on 2021-12-09 at 10:57.","This Thesis was approved for publication on 2021-12-09 at 13:26.","DSpace SAF Submission Ingestion Package generated from Vireo submission #17432 on 2022-04-06 at 17:18:05","Made available in DSpace on 2022-04-29T21:47:48Z (GMT). No. of bitstreams: 2 ZENG-THESIS-2021.pdf: 1376324 bytes, checksum: 8ef3728b5e032cb6b2d3b7451c93dce3 (MD5) LICENSE.txt: 4204 bytes, checksum: 02f6f03cd1403ec2d84926fa98beaba3 (MD5) Previous issue date: 2021-12-09","Embargo set by: Seth Robbins for item 123392 Lift date: 2024-04-29T21:47:53Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","U of I Only","Current methods for event representation ignore related events in a corpus-level global context. For a deep and comprehensive understanding of complex events, we introduce a new task, Event Network Embedding, which aims to represent events by capturing the connections among events. We propose a novel framework, Global Event Network Embedding (GENE), that encodes the event network with a multi-view graph encoder while preserving the graph topology and node semantics. The graph encoder is trained by minimizing both structural and semantic losses. We develop a new series of structured probing tasks, and show that our approach effectively outperforms baseline models on node typing, argument role classification, and event coreference resolution. As a direct application, We introduce a new task, Unsupervised Event Schema Graph Matching, which aims to align event instance graphs and event schema graphs by finding node correspondence. We develop the first benchmark and collect a dataset of 3,740 event instance graphs from IED-scenario news articles, 75 of which are paired with 4 event schema graphs by human annotation. Our analysis on this task sheds light on the shortcomings of current state-of-the-art models on this event understanding task."],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/114027"],"dc:language":["en","eng"],"dc:rights":["Copyright 2021 Qi Zeng"],"dc:subject":["Computer science"],"dc:title":["Event network embedding"],"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":"2026-07-22T22:24:54Z"}