Algorithm Research & Explore
|
1964-1968

Event extraction based on dynamic masked attention

Huang Xifeng
The 10th Research Institute of China Electronics Technology Group Corporation, Chengdu 610036, China

Abstract

Event extraction is an important and challenging task in NLP, completing the identification of event triggers and their arguments from the text. For multiple-event extraction tasks with multiple events in a sentence, this paper proposed a model based on a variant of attention mechanism called DyMAN, which could capture richer context representation and keep more valuable information than the normal attention. The experiments demonstrate that the proposed model can achieve state-of-the-art performance on the ACE 2005 corpus. Compared with the previous best model named JRNN, the DyMAN model achieved a 9.8% improvement in trigger word classification tasks, and a 4.5% improvement in factor classification tasks.

Foundation Support

国家重点研发计划重点专项项目(2017YFB1002104)
国家自然科学基金面上项目(61672162)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.12.0927
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 7
Section: Algorithm Research & Explore
Pages: 1964-1968
Serial Number: 1001-3695(2020)07-009-1964-05

Publish History

[2020-07-05] Printed Article

Cite This Article

黄细凤. 基于动态掩蔽注意力机制的事件抽取 [J]. 计算机应用研究, 2020, 37 (7): 1964-1968. (Huang Xifeng. Event extraction based on dynamic masked attention [J]. Application Research of Computers, 2020, 37 (7): 1964-1968. )

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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.

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