Abstract
Information Extraction is a challenging task in Natural Language Processing. There are several formats of information extraction that researchers mainly focused on. Typically, relation extraction and event extraction are two type of information extraction tasks that could facilitate many related business. In this work, we will introduce our solutions for the 2021 Language and Intelligence Challenge. We propose our methods for the relation extraction and event extraction tasks, respectively. The event extraction task is further divided into sentence level and document level. In relation extraction task, we propose a knowledge-based relation extraction method; in event extraction task, we propose a hybrid method which utilize event types and event triggers separately to extract event roles and event arguments. Finally, our solution ranked the second in the private leaderboard.
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References
Bekoulis, G., et al.: Joint entity recognition and relation extraction as a multi-head selection problem. Expert Syst. Appl. 114, 34–45 (2018)
Eberts, M., Ulges, A.: Span-based joint entity and relation extraction with transformer pre-training. arXiv preprint arXiv:1909.07755 (2019)
Zhong, Z., Chen, D.: A frustratingly easy approach for joint entity and relation extraction. arXiv preprint arXiv:2010.12812 (2020)
Peng, H., et al.: Learning from context or names? An empirical study on neural relation extraction. arXiv preprint arXiv:2010.01923 (2020)
Riloff, E.: Automatically constructing a dictionary for information extraction tasks. In: AAAI. Citeseer (1993)
Chieu, H.L., Ng, H.T.: A maximum entropy approach to information extraction from semi-structured and free text. AAAI/IAAI 2002, 786–791 (2002)
Ahn, D.: The stages of event extraction. In: Proceedings of the Workshop on Annotating and Reasoning about Time and Events (2006)
Yang, S., 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 (2019)
Li, D., et al.: Biomedical event extraction based on knowledge-driven tree-LSTM. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1 (Long and Short Papers) (2019)
Sha, L., et al.: Jointly extracting event triggers and arguments by dependency-bridge RNN and tensor-based argument interaction. In: Proceedings of the AAAI Conference on Artificial Intelligence (2018)
Wei, Z., et al.: A novel cascade binary tagging framework for relational triple extraction. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020)
Li, S., et al.: Duie: A large-scale Chinese dataset for information extraction. In: Tang, J., Kan, M.-Y., Zhao, D., Li, S., Zan, H. (eds.) NLPCC 2019. LNCS (LNAI), vol. 11839, pp. 791–800. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32236-6_72
Li, X., et al.: DuEE: A large-scale dataset for Chinese event extraction in real-world scenarios. In: Zhu, X., Zhang, M., Hong, Yu., He, R. (eds.) NLPCC 2020. LNCS (LNAI), vol. 12431, pp. 534–545. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-60457-8_44
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Dai, W., Hua, X., Lv, R., Bo, R., Chen, S. (2021). The Solution of Xiaomi AI Lab to the 2021 Language and Intelligence Challenge: Multi-format Information Extraction Task. In: Wang, L., Feng, Y., Hong, Y., He, R. (eds) Natural Language Processing and Chinese Computing. NLPCC 2021. Lecture Notes in Computer Science(), vol 13029. Springer, Cham. https://doi.org/10.1007/978-3-030-88483-3_40
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DOI: https://doi.org/10.1007/978-3-030-88483-3_40
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