1.School of Communication and Information Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065,China 2.Research Center of New Communication Technology Applications,Chongqing University of Posts and Telecommunications,Chongqing 400065,China
About author:YUAN Quan, born in 1976, M. S., senior engineer. His research interests include big data, natural language processing. TANG Chengliang, born in 1998, M. S. candidate. His research interests include big data, natural language processing.
YAO Y, YE D M, LI P. DocRED: a large-scale document-level relation extraction dataset[C]// Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA: ACL, 2019: 764-777. 10.18653/v1/P19-1074
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JIA R, WONG C, POON H. Document-level n-ary relation extraction with multiscale representation learning[C]// Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Stroudsburg, PA: ACL, 2019: 3693-3704. 10.18653/v1/n19-1370
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TANG H Z, CAO Y N, ZHANG Z Y, et al. HIN: hierarchical inference network for document-level relation extraction[C]// Proceedings of the 2020 Pacific-Asia Conference of Knowledge Discovery and Data Mining, LNCS 12084. Cham: Springer, 2020: 197-209. 10.48550/arXiv.2003.12754
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XU B F, WANG Q, LYU Y J, et al. Entity structure within and throughout: modeling mention dependencies for document-level relation extraction[C]// Proceedings of the 35th AAAI Conference on Artificial Intelligence. Palo Alto, CA: AAAI Press, 2021: 14149-14157. 10.1609/aaai.v35i16.17665
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VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]// Proceedings of the 31st International Conference on Neural Information Processing Systems. Red Hook, NY: Curran Associates Inc., 2017: 6000-6010.
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ZHOU W X, HUANG K, MA T Y, et al. Document-level relation extraction with adaptive thresholding and localized context pooling[C]// Proceedings of the 35th AAAI Conference on Artificial Intelligence. Palo Alto, CA: AAAI Press, 2021: 14612-14620. 10.1609/aaai.v35i16.17717
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SEO M, KEMBHAVI A, FARHADI A, et al. Bidirectional attention flow for machine comprehension[C]// Proceedings of the 5th International Conference on Learning Representations. Puerto Rico: ICLR, 2017: 1-13 .
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LI J, SUN Y P, JOHNSON R J, et al. BioCreative V CDR task corpus: a resource for chemical disease relation extraction[J]. Database, 2016, 2016: No.baw068. 10.1093/database/baw068
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LUAN Y, HE L, OSTENDORF M, et al. Multi-task identification of entities, relations, and coreference for scientific knowledge graph construction[C]// Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA: ACL, 2018: 3219-3232. 10.18653/v1/d18-1360
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RONNEBERGER O, FISCHER P, BROX T. U-Net: convolutional networks for biomedical image segmentation[C]// Proceedings of the 2015 Medical Image Computing and Computer-Assisted Intervention. Cham: Springer, 2015: 234-241. 10.1007/978-3-319-24574-4_28
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SUN Y F, CHENG C M, ZHANG Y H, et al. Circle loss: a unified perspective of pair similarity optimization[C]// Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2020: 6397-6406. 10.1109/cvpr42600.2020.00643
XIE T, YANG J A, LIU H. Chinese entity relation extraction based on multi-feature BERT model[J]. Computer Systems and Applications, 2021, 30(5):253-261. 10.15888/j.cnki.csa.007899
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LI B, YE W, SHENG Z, et al. Graph enhanced dual attention network for document-level relation extraction[C]// Proceedings of the 28th International Conference on Computational Linguistics. Stroudsburg, PA: ACL, 2020: 1551-1560. 10.18653/v1/2020.coling-main.136
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YE D M, LIN Y K, DU J J, et al. Coreferential reasoning learning for language representation[C]// Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA: ACL, 2020: 7170-7186. 10.18653/v1/2020.emnlp-main.582
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ZENG S, XU R X, CHANG B B, et al. Double graph based reasoning for document-level relation extraction[C]// Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA: ACL, 2020: 1630-1640. 10.18653/v1/2020.emnlp-main.127