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Document-Improved Hierarchical Modular Attention for Event Detection

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Knowledge Science, Engineering and Management (KSEM 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12275))

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Abstract

The task of event detection aims to find the event trigger for a sentence and identify the correct event type. Contextual information is crucial to event detection, which helps a model to identify the triggers better. Existing models utilizing contextual information only take document information as additional features for deep learning model, without considering the specific contribution of document information to trigger classification. In this paper, we propose a Document-Improved Hierarchical Modular Event Detection (DIHMED) model to extract hierarchical contextual information. Specifically, considering the relevance between event types, we build independent modules that combine the document-level information to express this relevance. Given the fact that events from the same document are often related, these modules can make better use of document-level information. We conduct several experiments on a Chinese political event dataset and the results show that our model can outperform the state-of-the-art models.

This work is supported by the National Natural Science Foundation of China (Grant No. U1934212).

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Authors and Affiliations

  1. School of Software Engineering, Tongji University, Shanghai, China

    Yiwei Ni, Qingfeng Du & Jincheng Xu

Authors
  1. Yiwei Ni
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  2. Qingfeng Du
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  3. Jincheng Xu
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Corresponding author

Correspondence to Qingfeng Du .

Editor information

Editors and Affiliations

  1. Deakin University, Geelong, VIC, Australia

    Gang Li

  2. University of Electronic Science and Technology of China, Chengdu, China

    Heng Tao Shen

  3. Beijing Institute of Technology, Beijing, China

    Ye Yuan

  4. Zhejiang Gongshang University, Hangzhou, China

    Xiaoyang Wang

  5. Zhejiang Normal University, Jinhua, China

    Huawen Liu

  6. National University of Defense Technology, Changsha, China

    Xiang Zhao

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Ni, Y., Du, Q., Xu, J. (2020). Document-Improved Hierarchical Modular Attention for Event Detection. In: Li, G., Shen, H., Yuan, Y., Wang, X., Liu, H., Zhao, X. (eds) Knowledge Science, Engineering and Management. KSEM 2020. Lecture Notes in Computer Science(), vol 12275. Springer, Cham. https://doi.org/10.1007/978-3-030-55393-7_29

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  • DOI: https://doi.org/10.1007/978-3-030-55393-7_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-55392-0

  • Online ISBN: 978-3-030-55393-7

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