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A convolution BiLSTM neural network model for Chinese event extraction

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  • Ying Zeng
  • Honghui Yang
  • Yansong Feng
  • Zheng Wang
  • Dongyan Zhao
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Publication date2/12/2016
Host publicationNatural Language Understanding and Intelligent Applications: 5th CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2016, and 24th International Conference on Computer Processing of Oriental Languages, ICCPOL 2016, Kunming, China, December 2–6, 2016, Proceedings
EditorsChin-Yew Lin, Nianwen Xue, Dongyan Zhao, Xuanjing Huang, Yansong Feng
Place of PublicationCham
PublisherSpringer
Pages275-287
Number of pages13
ISBN (Electronic)9783319504964
ISBN (Print)9783319504957
<mark>Original language</mark>English

Publication series

Name Lecture Notes in Computer Science
PublisherSpringer
Volume10102
ISSN (Print)0302-9743

Abstract

Chinese event extraction is a challenging task in information extraction. Previous approaches highly depend on sophisticated feature engineering and complicated natural language processing (NLP) tools. In this paper, we first come up with the language specific issue in Chinese event extraction, and then propose a convolution bidirectional LSTM neural network that combines LSTM and CNN to capture both sentence-level and lexical information without any hand-craft features. Experiments on ACE 2005 dataset show that our approaches can achieve competitive performances in both trigger labeling and argument role labeling.

Bibliographic note

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-50496-4_23