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International Conference on Neural Information Processing

ICONIP 2018: Neural Information Processing pp 255–264Cite as

Chinese Event Recognition via Ensemble Model

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11305))

Abstract

Event recognition is one of the most fundamental and critical field in information extraction. In this paper, Event recognition task can be divided into two sub-problems containing candidate event triggers identification and the classification of candidate event trigger words. Firstly, we use trigger vocabulary generated by trigger expansion to identify candidate event trigger, and then input sequences are generated according to the following three features: word embedding, POS (part of speech) and DP (dependency parsing). Finally multiclass classifier based on joint neural networks is introduced in the step of candidate trigger classification. The experiments in CEC (Chinese Emergency Corpus) have shown the superiority of our proposal model with a maximum F-measure of 80.55%.

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Acknowledgments

This paper is supported by the Natural Science Foundation of China, No. 61305053 and No. 61273328.

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Authors and Affiliations

  1. School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China

    Wei Liu, Zhenyu Yang & Zongtian Liu

Authors
  1. Wei Liu
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  2. Zhenyu Yang
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  3. Zongtian Liu
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Corresponding author

Correspondence to Wei Liu .

Editor information

Editors and Affiliations

  1. The Chinese Academy of Sciences, Beijing, China

    Long Cheng

  2. City University of Hong Kong, Kowloon, Hong Kong

    Andrew Chi Sing Leung

  3. Kobe University, Kobe, Japan

    Seiichi Ozawa

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Cite this paper

Liu, W., Yang, Z., Liu, Z. (2018). Chinese Event Recognition via Ensemble Model. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11305. Springer, Cham. https://doi.org/10.1007/978-3-030-04221-9_23

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  • DOI: https://doi.org/10.1007/978-3-030-04221-9_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04220-2

  • Online ISBN: 978-3-030-04221-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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