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Event Relation Extraction Using Type-Guided Attentive Graph Convolutional Networks

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Database Systems for Advanced Applications (DASFAA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13946))

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Abstract

Event relation extraction is a fundamental task in text mining, which has wide applications in event-centric natural language processing. However, most of the existing approaches can hardly model complicated contexts since they fail to use dependency-type knowledge in texts to assist in identifying implicit clues to event relations, leading to the sub-optimal performance on this task. To this end, we propose a novel type-guided attentive graph convolutional network for event relation extraction. Specifically, given the input text, the event-specific syntactic dependency graph is first constructed, from which both the local and global dependency knowledge related to events are derived. Then, a dependency-type guided attentive graph convolutional network is designed for learning representations of events, in which the local and global dependency information are utilized to effectively aggregate semantic context among texts. Finally, the event relation is predicted based on the representations of event pair and the representation of the whole text, completing the task of event relation extraction. The experimental results on multiple datasets show that our method significantly outperforms the state-of-the-art baselines. Moreover, further analysis reveals that our method can effectively capture complex relationships between long-distance events, improving accordingly the performance of event relation extraction.

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Acknowledgement

This work was supported by the National Social Science Fund of China under Grant No. 20BTQ068.

Author information

Authors and Affiliations

  1. Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, Hubei, China

    Ling Zhuang, Po Hu & Weizhong Zhao

  2. School of Computer Science, Central China Normal University, Wuhan, Hubei, China

    Ling Zhuang, Po Hu & Weizhong Zhao

  3. National Language Resources Monitoring and Research Center for Network Media, Central China Normal University, Wuhan, Hubei, China

    Ling Zhuang, Po Hu & Weizhong Zhao

Authors
  1. Ling Zhuang
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  2. Po Hu
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  3. Weizhong Zhao
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Corresponding authors

Correspondence to Po Hu or Weizhong Zhao .

Editor information

Editors and Affiliations

  1. Tianjin University, Tianjin, China

    Xin Wang

  2. University of Torino, Turin, Italy

    Maria Luisa Sapino

  3. POSTECH, Pohang, Korea (Republic of)

    Wook-Shin Han

  4. University of California Santa Barbara, Santa Barbara, CA, USA

    Amr El Abbadi

  5. University of Auckland, Auckland, New Zealand

    Gill Dobbie

  6. Tianjin University, Tianjin, China

    Zhiyong Feng

  7. Beijing University of Posts and Telecommunications, Beijing, China

    Yingxiao Shao

  8. The University of Queensland, Brisbane, QLD, Australia

    Hongzhi Yin

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Zhuang, L., Hu, P., Zhao, W. (2023). Event Relation Extraction Using Type-Guided Attentive Graph Convolutional Networks. In: Wang, X., et al. Database Systems for Advanced Applications. DASFAA 2023. Lecture Notes in Computer Science, vol 13946. Springer, Cham. https://doi.org/10.1007/978-3-031-30678-5_1

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  • DOI: https://doi.org/10.1007/978-3-031-30678-5_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-30677-8

  • Online ISBN: 978-3-031-30678-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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