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Regular Paper

Label-Aware Chinese Event Detection with Heterogeneous Graph Attention Network

Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100190, China
School of Cyber Security, University of Chinese Academy of Sciences, Beijing 100049, China
Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China
School of Cyber Security, Beijing University of Posts and Telecommunications, Beijing 100088, China
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Abstract

Event detection (ED) seeks to recognize event triggers and classify them into the predefined event types. Chinese ED is formulated as a character-level task owing to the uncertain word boundaries. Prior methods try to incorporate word-level information into characters to enhance their semantics. However, they experience two problems. First, they fail to incorporate word-level information into each character the word encompasses, causing the insufficient word-character interaction problem. Second, they struggle to distinguish events of similar types with limited annotated instances, which is called the event confusing problem. This paper proposes a novel model named Label-Aware Heterogeneous Graph Attention Network (L-HGAT) to address these two problems. Specifically, we first build a heterogeneous graph of two node types and three edge types to maximally preserve word-character interactions, and then deploy a heterogeneous graph attention network to enhance the semantic propagation between characters and words. Furthermore, we design a pushing-away game to enlarge the predicting gap between the ground-truth event type and its confusing counterpart for each character. Experimental results show that our L-HGAT model consistently achieves superior performance over prior competitive methods.

Keywords

Chinese event detectionheterogeneous graph attention network (HGAT)label embedding

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Journal of Computer Science and Technology
Volume 39 Issue 1,
January 2024
Pages 227-242
DOI: 10.1007/s11390-023-1541-6
Cite this article:
Cui S-Y, Yu B-W, Cong X, et al. Label-Aware Chinese Event Detection with Heterogeneous Graph Attention Network. Journal of Computer Science and Technology, 2024, 39(1): 227-242. https://doi.org/10.1007/s11390-023-1541-6

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Received: 23 April 2021
Accepted: 02 January 2023
Published: 25 January 2024
© Institute of Computing Technology, Chinese Academy of Sciences 2024
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