2026-09-20T16:33:30Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/110669 2023-07-11 col_2142_5131 col_2142_10761 com_2142_5130 com_2142_10755 com_2142_234

Ji, Heng Wen, Haoyang 2021-09-17T02:34:26Z 2021-09-17T02:34:26Z 2023-09-17T02:34:57Z 2021-04-23 2021-05 Temporal information extraction is a challenging task due to the inherent ambiguity of language. Event time plays an important role in temporal information extraction, which can help ground events into a timeline and can help other temporal information extraction tasks such as temporal relation extraction. However, explicit information for event time is not often expressed in a document. In this thesis, we first focus on a new event time representation that adopts the 4-tuple temporal representation proposed in the TAC-KBP temporal slot filling to resolve the uncertainty and sparsity problem. We then propose a graph neural network-based method to propagate local time information over constructed event graphs. We also study the event time in temporal relation extraction. We predict relative timestamps for events from event-event relation annotations and use those timestamps as additional features for training a temporal relation extraction system. We use the Stack-Propagation framework to jointly train the timestamps prediction and temporal relation extraction task. Finally, we demonstrate two knowledge extraction systems that have integrated the temporal information extraction models and show their effectiveness. Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2023-05-01 The student, Haoyang Wen, accepted the attached license on 2021-04-20 at 17:12. The student, Haoyang Wen, submitted this Thesis for approval on 2021-04-20 at 17:46. This Thesis was approved for publication on 2021-04-23 at 13:54. DSpace SAF Submission Ingestion Package generated from Vireo submission #16332 on 2021-09-16 at 17:03:06 Made available in DSpace on 2021-09-17T02:34:26Z (GMT). No. of bitstreams: 2 WEN-THESIS-2021.pdf: 1443356 bytes, checksum: ee52e2b25456241fcd490ed59fe1d896 (MD5) LICENSE.txt: 4208 bytes, checksum: 1d76b767a12ff0f59afd49bcfe155afd (MD5) Previous issue date: 2021-04-23 Embargo set by: Seth Robbins for item 118512 Lift date: 2023-09-17T02:34:57Z 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 application/pdf http://hdl.handle.net/2142/110669 en Copyright 2021 Haoyang Wen natural language processing information extraction time Event time representation, propagation and prediction in temporal information extraction text Thesis Computer Science Computer Science University of Illinois at Urbana-Champaign Thesis M.S.