Abstract
Event extraction (EE) can be divided into four subtasks: trigger identification, trigger classification, argument identification and argument classification. Most the previous studies focused on extracting flat events while neglecting overlapped events. Few models can handle both flat EE and overlapped EE. Sequence labeling models cannot resolve the event overlap problem. Multi-stage pipeline models introduce error propagation. One-stage joint extraction models cannot fully leverage the potential event information and their high complexity make them unsuitable for datasets with a wide variety of event-argument types. Therefore, we propose a unified model for event extraction, called UEE. Our method can use the potential event information for the argument classification subtask and utilize the predefined type-restricted decoding strategy to improve the model’s performance. We conduct experiments on three flat and overlapped EE benchmarks, namely FewFC, ACE05-CN and DuEE, and show that UEE achieves the state-of-the-art (SoTA) results. Moreover, the parameter number and inference speed of UEE are better than those of baselines in the same condition.
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References
Lin, Y., Ji, H., Huang, F., et al.: A joint neural model for information extraction with global features. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 7999–8009 (2020)
Sheng, J., Guo, S., Yu, B., et al.: CasEE: a joint learning framework with cascade decoding for overlapping event extraction. In: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pp. 164–174 (2021)
Cao, H., Li, J., Su, F., et al.: OneEE: a one-stage framework for fast overlapping and nested event extraction. In: Proceedings of the 29th International Conference on Computational Linguistics, pp. 1953–1964 (2022)
Guan, S., Cheng, X., Bai, L., et al.: What is event knowledge graph: a survey. In: IEEE Trans. Knowl. Data Eng. 35(7), 7569–7589 (2023)
Zhang, W., Zhao, X., Zhao, L., et al.: DRL4IR: 2nd workshop on deep reinforcement learning for information retrieval. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 2681–2684 (2021)
Li, M., Xu, R., Wang, S., et al.: Clip-event: connecting text and images with event structures. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16420–16429 (2022)
Yang, S., Feng, D., Qiao, L., et al.: Exploring pre-trained language models for event extraction and generation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 5284–5294 (2019)
Li, F., Peng, W., Chen, Y., et al.: Event extraction as multi-turn question answering. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 829–838 (2020)
Devlin, J., Chang, M.W., Lee, K., et al.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)
Su, J., Murtadha, A., Pan, S., et al.: Global pointer: novel efficient span-based approach for named entity recognition. arXiv preprint arXiv:2208.03054 (2022)
Du, X., Cardie, C.: Event extraction by answering (almost) natural questions. arXiv preprint arXiv:2004.13625 (2020)
Li, J., Fei, H., Liu, J., et al.: Unified named entity recognition as word-word relation classification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 10965–10973 (2022)
Zhou, Y., Chen, Y., Zhao, J., et al.: What the role is vs. what plays the role: semi-supervised event argument extraction via dual question answering. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 14638–14646 (2021)
Li, X., Li, F., Pan, L., et al.: DuEE: a large-scale dataset for Chinese event extraction in real-world scenarios. In: Natural Language Processing and Chinese Computing: 9th CCF International Conference, pp. 534–545 (2020)
Acknowledgments
This research is financially supported by Science and Technology Committee of Shanghai Municipality (STCSM) (Science and Technology Program Grants 22511104800 and 22DZ1204903).
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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Duan, Z., Guo, Y., Yao, C., Chen, X. (2024). UEE: A Unified Model for Event Extraction. In: Huang, DS., Si, Z., Zhang, Q. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2024. Lecture Notes in Computer Science(), vol 14877. Springer, Singapore. https://doi.org/10.1007/978-981-97-5669-8_28
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DOI: https://doi.org/10.1007/978-981-97-5669-8_28
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