Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 10
2967-2971,3006

Event detection based on dependency information and graph convolutional network

Zhang Ziyue1
Wang Yu2,3
Xu Jian1
1. School of Computer Science & Engineering, Nanjing University of Science & Technology, Nanjing 210094, China
2. Science & Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha 410003, China
3. The 28th Research Institute of China Electronics Technology Group Corporation, Nanjing 210007, China
DOI: 10.19734/j.issn.1001-3695.2023.03.0097

Abstract

The fine-grained event detection task at the sentence level aims at identifying and classifying triggers. To solve the problem of over-smoothing and lack of dependency type information in existing event detection methods, this paper proposed an event detection method based on dependency information and graph convolutional network(GCN). This method firstly used bi-directional long short-term memory(Bi-LSTM) networks to encode the sentence, and constructed a multi-order syntactic graph and a dependent syntactic graph based on the dependency analysis. Then it used GCN to aggregate the sentences' dependency information, which effectively utilized the multi-hop information and the dependent type information. On automatic content extraction(ACE) dataset, the proposed method achieved 81.7% and 78.6% F1 values in the two subtasks of trigger identification and classification. Results show that the proposed method can capture event information in sentences more effectively, and improve the effect of event detection.

Foundation Support

国防基础科研计划国防科技重点实验室稳定支持项目
国家自然科学基金资助项目

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.03.0097
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 10
Section: Algorithm Research & Explore
Pages: 2967-2971,3006
Serial Number: 1001-3695(2023)10-013-2967-05

Publish History

[2023-07-05] Accepted Paper
[2023-10-05] Printed Article

Cite This Article

张紫月, 王羽, 徐建. 基于图卷积网络融合依存信息的事件检测方法 [J]. 计算机应用研究, 2023, 40 (10): 2967-2971,3006. (Zhang Ziyue, Wang Yu, Xu Jian. Event detection based on dependency information and graph convolutional network [J]. Application Research of Computers, 2023, 40 (10): 2967-2971,3006. )

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  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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