References
Du X, Cardie C. Document-level event role filler extraction using multi-granularity contextualized encoding. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2020, 8010–8020
McLean V. Fourth message understanding conference (MUC-4). In: Proceedings of Fourth Message Understanding Conference (MUC-4). 1992
Wadden D, Wennberg U, Luan Y, Hajishirzi H. Entity, relation, and event extraction with contextualized span representations. In: Proceedings of 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. 2019, 5784–5789
Du X, Rush A M, Cardie C. GRIT: Generative role-filler transformers for document-level event entity extraction. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics. 2021, 634–644
Paolini G, Athiwaratkun B, Krone J, Jie M, Achille A, Anubhai R, dos Santos C N, Xiang B, Soatto S. Structured prediction as translation between augmented natural languages. In: Proceedings of the 9th International Conference on Learning Representations. 2021, 1–26
Huang K H, Tang S, Peng N. Document-level entity-based extraction as template generation. In: Proceedings of 2021 Conference on Empirical Methods in Natural Language Processing. 2021, 5257–5269
Chen Y, Gantt W, Gu W, Chen T, White A, Van Durme B. Iterative document-level information extraction via imitation learning. In: Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics. 2023, 1858–1874
Acknowledgements
This work was supported by the National Natural Science Foundation of China (Grant Nos. U21B2027, U23A20388, 62266028), the Yunnan Provincial Major Science and Technology Special Plan Projects (202302AD080003, 202202AD080003, 202303AP140008), the Yunnan Fundamental Research Projects (202301AS070047), and the Kunming University of Science and Technology’s “Double First-rate” Construction Joint Project (202201BE070001-021).
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Element relational graph-augmented multi-granularity contextualized encoding for document-level event role filler extraction
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Zhu, E., Yu, Z., Huang, Y. et al. Element relational graph-augmented multi-granularity contextualized encoding for document-level event role filler extraction. Front. Comput. Sci. 19, 192326 (2025). https://doi.org/10.1007/s11704-024-3701-4
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DOI: https://doi.org/10.1007/s11704-024-3701-4