A Novel Joint Framework for Multiple Chinese Events Extraction

Nuo Xu, Haihua Xie, Dongyan Zhao


Abstract
Event extraction is an essential yet challenging task in information extraction. Previous approaches have paid little attention to the problem of roles overlap which is a common phenomenon in practice. To solve this problem, this paper defines event relation triple to explicitly represent relations among triggers, arguments and roles which are incorporated into the model to learn their inter-dependencies. The task of argument extraction is converted to event relation triple extraction. A novel joint framework for multiple Chinese event extraction is proposed which jointly performs predictions for event triggers and arguments based on shared feature representations from pre-trained language model. Experimental comparison with state-of-the-art baselines on ACE 2005 dataset shows the superiority of the proposed method in both trigger classification and argument classification.
Anthology ID:
2020.ccl-1.88
Volume:
Proceedings of the 19th Chinese National Conference on Computational Linguistics
Month:
October
Year:
2020
Address:
Haikou, China
Venue:
CCL
SIG:
Publisher:
Chinese Information Processing Society of China
Note:
Pages:
950–961
Language:
English
URL:
https://www.aclweb.org/anthology/2020.ccl-1.88
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.ccl-1.88.pdf