Graph neural networks with selective attention and path reasoning for document-level relation extraction
Author:
Funder
Key Technologies Research and Development Program
Natural Science Foundation of Anhui Province
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10489-024-05448-4.pdf
Reference47 articles.
1. Distiawan B, Weikum G, Qi J, Zhang R (2019) Neural relation extraction for knowledge base enrichment. In: Proceedings of the 57th annual meeting of the association for computational linguistics. https://doi.org/10.18653/v1/p19-1023
2. Yu M, Yin W, Hasan KS, dos Santos C, Xiang B, Zhou B (2017) Improved neural relation detection for knowledge base question answering. In: Proceedings of the 55th annual meeting of the association for computational linguistics. https://doi.org/10.18653/v1/P17-1053
3. Lai T, Cheng L, Wang D, Ye H, Zhang W (2022) Rman: Relational multi-head attention neural network for joint extraction of entities and relations. Appl Intell 52(3):3132–3142. https://doi.org/10.1007/s10489-021-02600-2
4. Li X, Li Y, Yang J, Liu H, Hu P (2022) A relation aware embedding mechanism for relation extraction. Appl Intell, pp 1–10. https://doi.org/10.1007/s10489-021-02699-3
5. Christopoulou F, Miwa M, Ananiadou S (2018) A walk-based model on entity graphs for relation extraction. In: Proceedings of the 56th annual meeting of the association for computational linguistics. https://doi.org/10.18653/v1/P18-2014
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