Title: RCRE: radical-aware causal relationship extraction model oriented the medical field

Authors: Xiaoqing Li; Guangli Zhu; Zhongliang Wei; Shunxiang Zhang

Addresses: School of Computer Science and Engineering, Anhui University of Science and Technology, Huainan, China; Artificial Intelligence Research Institute of Hefei Comprehensive National Science Center, Hefei, China ' School of Computer Science and Engineering, Anhui University of Science and Technology, Huainan, China; Artificial Intelligence Research Institute of Hefei Comprehensive National Science Center, Hefei, China ' School of Computer Science and Engineering, Anhui University of Science and Technology, Huainan, China; Artificial Intelligence Research Institute of Hefei Comprehensive National Science Center, Hefei, China ' School of Computer Science and Engineering, Anhui University of Science and Technology, Huainan, China; Artificial Intelligence Research Institute of Hefei Comprehensive National Science Center, Hefei, China

Abstract: In the massive medical texts, the accuracy of causal relationship extraction is relatively low because of its special characteristic, a high correlation between semantics and radicals. To improve the extraction accuracy, this paper proposes a radical-aware causal relationship extraction model, which is oriented to the medical field. The BERT pre-training model is used to extract character-level features, which contain rich context information. To further deeply capture the semantics of characters, the Word2Vec model is utilised to extract radical features. Finally, the above two features are concatenated and passed into the extraction model to obtain the extraction results. Experimental results show that the proposed model can improve the accuracy of causal relationship extraction in medical texts.

Keywords: causal relationship extraction; the medical field; radical features; the BERT model; the Word2Vec model.

DOI: 10.1504/IJCSE.2023.131511

International Journal of Computational Science and Engineering, 2023 Vol.26 No.3, pp.243 - 254

Received: 15 Jan 2022
Received in revised form: 10 May 2022
Accepted: 18 May 2022
Published online: 15 Jun 2023 *

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