计算机工程与应用2024，Vol.60Issue(14)：152-161,10.DOI:10.3778/j.issn.1002-8331.2305-0453
融合多粒度语义信息和知识图谱的中文医疗问答匹配模型
Chinese Medical Q&A Matching Model Based on Multi-Granularity Semantic Information and Knowledge Graph
管立本 1李实1
作者信息
- 1. 东北林业大学计算机与控制工程学院,哈尔滨 150040
- 折叠
摘要
Abstract
Chinese medical Q&A is easily affected by the noise of medical-specific terminology,making it more challenging than open-domain Q&A.Previous studies on Chinese medical Q&A mainly relied on character-level fine-grained informa-tion,neglecting word-level coarse-grained information that carries more semantic information.In addition,introducing external medical knowledge graph can further enrich the fine-grained information in Q&A sentences,but most existing studies usually adopt a simple way of joint representation of sentences and external knowledge.Therefore,this paper proposes a Chinese medical Q&A matching model based on multi-granularity semantic information and knowledge graph(CMQA-MGSI).The model employs a Lattice network to select the most relevant character-level and word-level sequences from the Q&A sentences,and leverages Word2Vec and BERT to enhance the semantic information;to better exploit the external domain knowledge,a dual-channel attention mechanism is devised to capture the multi-angle knowledge repre-sentations between the Q&A sentences and the entity embeddings and relation embeddings in the knowledge graph.Experi-ments on the cMedQA1.0 and cMedQA2.0 datasets demonstrate that the proposed model outperforms existing Chinese medical Q&A matching models.关键词
中文医疗问答/多粒度信息/知识图谱/Lattice网络/注意力机制Key words
Chinese medical Q&A/multi-granularity information/knowledge graph/Lattice network/attention mechanism分类
信息技术与安全科学引用本文复制引用
管立本,李实..融合多粒度语义信息和知识图谱的中文医疗问答匹配模型[J].计算机工程与应用,2024,60(14):152-161,10.
