Edge-Enhanced Graph Convolution Networks for Event Detection with Syntactic Relation

Shiyao Cui, Bowen Yu, Tingwen Liu, Zhenyu Zhang, Xuebin Wang, Jinqiao Shi


Abstract
Event detection (ED), a key subtask of information extraction, aims to recognize instances of specific event types in text. Previous studies on the task have verified the effectiveness of integrating syntactic dependency into graph convolutional networks. However, these methods usually ignore dependency label information, which conveys rich and useful linguistic knowledge for ED. In this paper, we propose a novel architecture named Edge-Enhanced Graph Convolution Networks (EE-GCN), which simultaneously exploits syntactic structure and typed dependency label information to perform ED. Specifically, an edge-aware node update module is designed to generate expressive word representations by aggregating syntactically-connected words through specific dependency types. Furthermore, to fully explore clues hidden from dependency edges, a node-aware edge update module is introduced, which refines the relation representations with contextual information.These two modules are complementary to each other and work in a mutual promotion way. We conduct experiments on the widely used ACE2005 dataset and the results show significant improvement over competitive baseline methods.
Anthology ID:
2020.findings-emnlp.211
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2020
Month:
November
Year:
2020
Address:
Online
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2329–2339
Language:
URL:
https://aclanthology.org/2020.findings-emnlp.211
DOI:
10.18653/v1/2020.findings-emnlp.211
Bibkey:
Cite (ACL):
Shiyao Cui, Bowen Yu, Tingwen Liu, Zhenyu Zhang, Xuebin Wang, and Jinqiao Shi. 2020. Edge-Enhanced Graph Convolution Networks for Event Detection with Syntactic Relation. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 2329–2339, Online. Association for Computational Linguistics.
Cite (Informal):
Edge-Enhanced Graph Convolution Networks for Event Detection with Syntactic Relation (Cui et al., Findings 2020)
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PDF:
https://aclanthology.org/2020.findings-emnlp.211.pdf
Code
 cuishiyao96/eegcned