基于知识图谱与关键词注意机制的中文医疗问答匹配方法

针对当前中文医疗领域高质量问答数据缺乏的问题,提出基于知识图谱与关键词注意机制的中文医疗问答匹配方法.首先,引入医学知识图谱,得到知识增强的句子特征.然后,加入关键词注意力机制,强调问题和答案句子之间的相互影响.在2个公开的中文医疗问答数据集cMedQA与webMedQA上的实验表明,当样本数据量较小时,文中方法的优势明显.消融实验也验证每个新增模块对文中方法的性能均有一定程度的提升.

Chinese Medical Question Answering Matching Method Based on Knowledge Graph and Keyword Attention Mechanism

Due to the lack of high-quality question and answer data in Chinese medical field, a Chinese medical question answering matching method combining knowledge graph and keyword attention mechanism is proposed. Firstly, the medical knowledge graph is introduced into the bidirectional encoder representation from transformers(BERT) model to obtain knowledge-enhanced sentence features, and a keyword attention mechanism is employed to emphasize the interaction between question and answer sentences. The experimental results on two open Chinese medical question-answer datasets, cMedQA and webMedQA , show that the proposed model is obviously better , especially for the small amount of samples. The ablation experiment also verifies that each of the new modules improve the performance of BERT to a certain extent.