Improving Shared Argument Identification in Japanese Event Knowledge Acquisition

Yin Jou Huang, Sadao Kurohashi


Abstract
Event knowledge represents the knowledge of causal and temporal relations between events. Shared arguments of event knowledge encode patterns of role shifting in successive events. A two-stage framework was proposed for the task of Japanese event knowledge acquisition, in which related event pairs are first extracted, and shared arguments are then identified to form the complete event knowledge. This paper focuses on the second stage of this framework, and proposes a method to improve the shared argument identification of related event pairs. We constructed a gold dataset for shared argument learning. By evaluating our system on this gold dataset, we found that our proposed model outperformed the baseline models by a large margin.
Anthology ID:
W17-2704
Volume:
Proceedings of the Events and Stories in the News Workshop
Month:
August
Year:
2017
Address:
Vancouver, Canada
Venues:
EventStory | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
21–30
Language:
URL:
https://www.aclweb.org/anthology/W17-2704
DOI:
10.18653/v1/W17-2704
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/W17-2704.pdf