摘要
Abstract
In order to solve the problem of poor entity relation extraction caused by the complexity and diversity of entity relations in traditional Chinese medicine(TCM),a relation extraction model(r-BERT-BiLSTM-attention-textCNN,RBBAT)based on attention mechanism and multi-model fusion is proposed.The model is composed of relation extraction pre-training model(r-BERT),bidirectional long/short-term memory neural network(BiLSTM),Attention layer and TextCNN;In the experiment,the relevant medical records of the department of gastroenterology published on various medical record platforms in recent years were selected,and five entity relationships were extracted,including symptom-disease name,symptom-syndrome,tongue-syndrome,pulse-syndrome,and syndrome-treatment.The experimental results show that compared with several commonly used relation extraction models,the proposed fusion model has the best extraction ability in the four entity relations of symptom-disease name,symptom-syndrome,tongue picture-syndrome,and syndrome-treatment method.关键词
关系抽取/中医案例/预训练模型/注意力机制/TextCNNKey words
relation extraction/TCM case/pre-training model/attention mechanism/TextCNN分类
信息技术与安全科学
