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Large language models can perform well on general natural language tasks, but their effectiveness is still suboptimal for information extraction (IE).
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Luan, Y., He, L., Ostendorf, M., Hajishirzi, H.: Multi-task identification of entities, relations, and coreference for scientific knowledge graph construction. In: Riloff, E., Chiang, D., Hockenmaier, J., Tsujii, J. (eds.) Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, October 31 - November 4, 2018. pp. 3219–3232. Association for Computational Linguistics (2018). https://doi.org/10.18653/V1/D18-1360, https://doi.org/10.18653/v1/d18-1360
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Li, S., He, W., Shi, Y., Jiang, W., Liang, H., Jiang, Y., Zhang, Y., Lyu, Y., Zhu, Y.: Duie: A large-scale chinese dataset for information extraction. In: Tang, J., Kan, M., Zhao, D., Li, S., Zan, H. (eds.) Natural Language Processing and Chinese Computing - 8th CCF International Conference, NLPCC 2019, Dunhuang, China, October 9-14, 2019, Proceedings, Part II. Lecture Notes in Computer Science, vol. 11839, pp. 791–800. Springer (2019). https://doi.org/10.1007/978-3-030-32236-6_72, https://doi.org/10.1007/978-3-030-32236-6_72
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Wang, H., He, Z., Ma, J., Chen, W., Zhang, M.: Ipre: a dataset for inter-personal relationship extraction. In: Natural Language Processing and Chinese Computing: 8th CCF International Conference, NLPCC 2019, Dunhuang, China, October 9–14, 2019, Proceedings, Part II 8. pp. 103–115. Springer (2019)
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Kocaman, V., Talby, D.: Biomedical named entity recognition at scale. In: Bimbo, A.D., Cucchiara, R., Sclaroff, S., Farinella, G.M., Mei, T., Bertini, M., Escalante, H.J., Vezzani, R. (eds.) Pattern Recognition. ICPR International Workshops and Challenges - Virtual Event, January 10-15, 2021, Proceedings, Part I. Lecture Notes in Computer Science, vol. 12661, pp. 635–646. Springer (2020). https://doi.org/10.1007/978-3-030-68763-2_48, https://doi.org/10.1007/978-3-030-68763-2_48
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Satyapanich, T., Ferraro, F., Finin, T.: CASIE: extracting cybersecurity event information from text. In: The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, February 7-12, 2020. pp. 8749–8757. AAAI Press (2020). https://doi.org/10.1609/AAAI.V34I05.6401, https://doi.org/10.1609/aaai.v34i05.6401
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Chen, C., Li, C.: ZS-BERT: towards zero-shot relation extraction with attribute representation learning. In: Toutanova, K., Rumshisky, A., Zettlemoyer, L., Hakkani-Tür, D., Beltagy, I., Bethard, S., Cotterell, R., Chakraborty, T., Zhou, Y. (eds.) Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2021, Online, June 6-11, 2021. pp. 3470–3479. Association for Computational Linguistics (2021). https://doi.org/10.18653/V1/2021.NAACL-MAIN.272, https://doi.org/10.18653/v1/2021.naacl-main.272
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Xue, L., Constant, N., Roberts, A., Kale, M., Al-Rfou, R., Siddhant, A., Barua, A., Raffel, C.: mt5: A massively multilingual pre-trained text-to-text transformer. In: HLT-NAACL 2021. pp. 483–498. Association for Computational Linguistics (2021)
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Liu, X., Huang, H., Shi, G., Wang, B.: Dynamic prefix-tuning for generative template-based event extraction. In: Muresan, S., Nakov, P., Villavicencio, A. (eds.) Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2022, Dublin, Ireland, May 22-27, 2022. pp. 5216–5228. Association for Computational Linguistics (2022). https://doi.org/10.18653/V1/2022.ACL-LONG.358, https://doi.org/10.18653/v1/2022.acl-long.358
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