Attention-based deep residual learning network for entity relation extraction in Chinese EMRs

Author:

Zhang Zhichang,Zhou Tong,Zhang Yu,Pang Yali

Publisher

Springer Science and Business Media LLC

Subject

Health Informatics,Health Policy,Computer Science Applications

Reference20 articles.

1. Lin Y, Liu Z, Sun M. Neural relation extraction with multi-lingual attention. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics:Long Papers-Volume 1. Vancouver: Association for Computational Linguistics: 2017. p. 34–43.

2. Zheng S, Wang F, Bao H, et al.Joint extraction of entities and relations based on a novel tagging scheme. 2017. arXiv preprint arXiv:1706.05075 [cs.CL]. https://arxiv.org/abs/1706.05075 .

3. Takamatsu S, Sato I, Nakagawa H. Reducing wrong labels in distant supervision for relation extraction. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers-Volume 1. Jeju Island: Association for Computational Linguistics: 2012. p. 721–9.

4. Miller S, Fox H, Ramshaw L, Weischedel R. A novel use of statistical parsing to extract information from text. In: Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference. Seattle: Association for Computational Linguistics: 2000. p. 226–33.

5. Kambhatla N. Combining lexical, syntactic, and semantic features with maximum entropy models for extracting relations. In: Proceedings of the ACL 2004 on Interactive poster and demonstration sessions. Barcelona: Association for Computational Linguistics: 2004. p. 22–25.

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