ETCGN: entity type-constrained graph networks for document-level relation extraction
Author:
Funder
Sichuan Science and Technology Planning Project
Chengdu Science and Technology Project
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s13042-024-02293-2.pdf
Reference40 articles.
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3. Cheng Q, Liu J, Qu X, Zhao J, Liang J, Wang Z, et al. (2021) Hacred: A large-scale relation extraction dataset toward hard cases in practical applications. In: Zong C, Xia F, Li W, Navigli R (eds) Findings of the association for computational linguistics: ACL/IJCNLP 2021, online event, August 1–6, 2021 (vol ACL/IJCNLP 2021). Association for Computational Linguistics, pp 2819–2831. https://doi.org/10.18653/v1/2021.findings-acl.249
4. Christopoulou F, Miwa M, Ananiadou S (2019) Connecting the dots: Documentlevel neural relation extraction with edge-oriented graphs. In: Inui K, Jiang J, Ng V, Wan X (eds) Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing, EMNLP-IJCNLP 2019, Hong Kong, China, November 3–7, 2019. Association for Computational Linguistics, pp 4924–4935. https://doi.org/10.18653/v1/D19-1498
5. Devlin J, Chang M, Lee K, Toutanova K (2019) BERT: pre-training of deep bidirectional transformers for language understanding. In: Burstein J, Doran C, Solorio T (eds) Proceedings of the 2019 conference of the north American chapter of the association for computational linguistics: human language technologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2-7, 2019, vol 1 (long and short papers). Association for Computational Linguistics, pp 4171–4186. https://doi.org/10.18653/v1/n19-1423
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