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Seq2EG: a novel and effective event graph parsing approach for event extraction

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Abstract

Event extraction is a fundamental task in information extraction. Most previous approaches typically transform event extraction into two subtasks: trigger classification and argument classification, and solve them via classification-based methods, which suffer from some inherent drawbacks. To overcome these issues, in this paper, we propose a novel event extraction model Seq2EG by first formulating event extraction as an event graph parsing problem, and then exploiting a pre-trained sequence-to-sequence (seq2seq) model to transduce an input sentence into an accurate event graph without the need for trigger words. Based on the generative event graph parsing formulation, our model Seq2EG can explicitly model the multiple event correlations and argument sharing and can naturally incorporate some graph-structured features and the rich semantic information conveyed by the labels of event types and argument roles. Extensive experimental results on the public ACE2005 dataset show that our approach outperforms all previous state-of-the-art models for event extraction by a large margin, respectively, obtaining an improvement of 3.4% F1 score for event detection and an improvement of 4.7% F1 score for argument classification over the best baselines.

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Notes

  1. The source code will be publicly released upon acceptance.

  2. https://github.com/luyaojie/Text2Event.

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Acknowledgements

We thank all reviewers for the valuable comments. This work is supported by project 62277031 under the National Science Foundation of China and project 22AYY013 under the National Social Science Foundation of China.

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Authors and Affiliations

  1. School of Computer and Electronic Information, Nanjing Normal University, Nanjing, China

    Haotong Sun, Junsheng Zhou, Li Kong, Yanhui Gu & Weiguang Qu

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  1. Haotong Sun
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  2. Junsheng Zhou
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  3. Li Kong
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  4. Yanhui Gu
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Correspondence to Junsheng Zhou.

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Sun, H., Zhou, J., Kong, L. et al. Seq2EG: a novel and effective event graph parsing approach for event extraction. Knowl Inf Syst 65, 4273–4294 (2023). https://doi.org/10.1007/s10115-023-01898-3

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