Event time representation, propagation and prediction in temporal information extraction

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.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2021

Author dc:creator

• Wen, Haoyang

Contributors dc:contributor

• Ji, Heng

• natural language processing
• information extraction
• time

Statement dc:rights

• Copyright 2021 Haoyang Wen

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/110669

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/110669