Exploiting the Entity Type Sequence to Benefit Event Detection

Yuze Ji, Youfang Lin, Jianwei Gao, Huaiyu Wan


Abstract
Event Detection (ED) is one of the most important task in the field of information extraction. The goal of ED is to find triggers in sentences and classify them into different event types. In previous works, the information of entity types are commonly utilized to benefit event detection. However, the sequential features of entity types have not been well utilized yet in the existing ED methods. In this paper, we propose a novel ED approach which learns sequential features from word sequences and entity type sequences separately, and combines these two types of sequential features with the help of a trigger-entity interaction learning module. The experimental results demonstrate that our proposed approach outperforms the state-of-the-art methods.
Anthology ID:
K19-1057
Volume:
Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)
Month:
November
Year:
2019
Address:
Hong Kong, China
Venue:
CoNLL
SIG:
SIGNLL
Publisher:
Association for Computational Linguistics
Note:
Pages:
613–623
Language:
URL:
https://www.aclweb.org/anthology/K19-1057
DOI:
10.18653/v1/K19-1057
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PDF:
http://aclanthology.lst.uni-saarland.de/K19-1057.pdf