RMAN: Relational multi-head attention neural network for joint extraction of entities and relations-Reference-Cited by-同舟云学术

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RMAN: Relational multi-head attention neural network for joint extraction of entities and relations

Published:2021-07-01 Issue: Volume: Page:
ISSN:0924-669X
Container-title:Applied Intelligence
language:en
Short-container-title:Appl Intell

Author:

Lai Taiqu,Cheng Lianglun,Wang Depei,Ye Haiming,Zhang Weiwen^ORCID

Funder

Key-Area Research and Development Program of Guangdong Province

Key Program of NSFC-Guangdong Joint Funds

Science and Technology Projects of Guangzhou

Program of Marine Economy Development (Six Marine Industries) Special Foundation of Department of Natural Resources of Guangdong Province

National Natural Science Foundation of China

Top Youth Talent Project of Zhujiang Talent Program

National Key R & D project

R & D projects in key areas of Guangdong Province

Guangdong Provincial Key Laboratory of Cyber-Physical System

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Link

https://link.springer.com/content/pdf/10.1007/s10489-021-02600-2.pdf

Reference37 articles.

1. Adel H, Schütze H (2017) Global normalization of convolutional neural networks for joint entity and relation classification. arXiv:170707719

2. Akkasi A, Varoğlu E, Dimililer N (2018) Balanced undersampling: a novel sentence-based undersampling method to improve recognition of named entities in chemical and biomedical text. Appl Intell 48 (8):1965–1978

3. Bunescu R, Mooney R (2005) A shortest path dependency kernel for relation extraction. In: Proceedings of human language technology conference and conference on empirical methods in natural language processing, pp 724–731

4. Cao P, Chen Y, Liu K, Zhao J, Liu S (2018) Adversarial transfer learning for chinese named entity recognition with self-attention mechanism. In: Proceedings of the 2018 conference on empirical methods in natural language processing, pp 182–192

5. Christopoulou F, Tran T T, Sahu S K, Miwa M, Ananiadou S (2020) Adverse drug events and medication relation extraction in electronic health records with ensemble deep learning methods. J Am Med Inform Assoc 27(1):39–46

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