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Character-Based LSTM-CRF with Semantic Features for Chinese Event Element Recognition

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Artificial Neural Networks and Machine Learning – ICANN 2020 (ICANN 2020)

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Abstract

Event element recognition is a significant task in event-based information extraction. In this paper, we propose an event element recognition model based on character-level embedding with semantic features. By extracting character-level features, the proposed model can capture more information of words. Our results show that joint character Convolutional Neural Networks (CNN) and character Bi-directional Long Short-Term Memory Networks (Bi-LSTM) is superior to single character-level model. In addition, adding semantic features such as POS (part-of-speech) and DP (dependency parsing) tends to improve the effect of recognition. We evaluated different methods in CEC (Chinese Emergency Corpus), and the experimental results show that our model can achieve good performance, and the F value of element recognition was 77.17%.

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Notes

  1. 1.

    CEC is a Chinese emergency corpus, its open source address is https://github.com/DaseLab/CEC-Corpus.

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Acknowledgments

This paper was supported by The National key Research and Development Program of China (No. 2017YFE0117500), The Ministry of Education in China Project of Humanities and Social Sciences for Youth Scholars (No. 19YJCZH031), The National Social Science Fund Major of China (No. 19ZDA301).

Author information

Authors and Affiliations

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

    Wei Liu, Yusen Wu, Lei Jiang & Weimin Li

  2. School of Mathematics and Information, Shanghai Lixin University of Accounting and Finance, Shanghai, 201620, China

    Jianfeng Fu

Authors
  1. Wei Liu
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  2. Yusen Wu
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  3. Lei Jiang
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  4. Jianfeng Fu
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  5. Weimin Li
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Corresponding author

Correspondence to Wei Liu .

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Editors and Affiliations

  1. Department of Applied Informatics, Comenius University in Bratislava, Bratislava, Slovakia

    Igor Farkaš

  2. Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark

    Paolo Masulli

  3. Department of Informatics, University of Hamburg, Hamburg, Germany

    Stefan Wermter

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Liu, W., Wu, Y., Jiang, L., Fu, J., Li, W. (2020). Character-Based LSTM-CRF with Semantic Features for Chinese Event Element Recognition. In: Farkaš, I., Masulli, P., Wermter, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2020. ICANN 2020. Lecture Notes in Computer Science(), vol 12396. Springer, Cham. https://doi.org/10.1007/978-3-030-61609-0_64

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  • DOI: https://doi.org/10.1007/978-3-030-61609-0_64

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