Abstract
Extracting medical entity relations from Traditional Chinese Medicine (TCM) related article is crucial to connect domain knowledge between TCM with modern medicine. Herb accounts for the majority of Traditional Chinese Medicine, so our work mainly focuses on herb. The problem would be effectively solved by extracting herb-related entity relations from PubMed literature. In order to realize the entity relation mining, we propose a novel deep-learning model with improved layers without manual feature engineering. We design a new segment attention mechanism based on Convolutional Neural Network, which enables extracting local semantic features through word embedding. Then we classify the relations by connecting different embedding features. We first test this method on the Chemical-Induced Disease task and the experiment show better result comparing to other state-of-the-art deep learning methods. Further, we apply this method to a herbal-related data set (Herbal-Disease and Herbal Chemistry, HD-HC) constructed from PubMed to explore entity relation classification. The experiment shows superior results than other baseline methods.
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Funding
This work is supported by the Development Project of Jilin Province of China (Nos.20200801033GH, YDZJ202101ZYTS128), Jilin Provincial Key Laboratory of Big Data Intelligent Computing (No.20180622002JC), The Fundamental Research Funds for the Central University, JLU.
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Bai, T., Guan, H., Wang, S. et al. Traditional Chinese medicine entity relation extraction based on CNN with segment attention. Neural Comput & Applic 34, 2739–2748 (2022). https://doi.org/10.1007/s00521-021-05897-9
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DOI: https://doi.org/10.1007/s00521-021-05897-9