National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [61573163, 71571084]; Fundamental Research Funds for the Central UniversitiesFundamental Research Funds for the Central Universities [CCNU16A02024]; Wuhan Youth Science and Technology Plan
机构署名：
本校为第一且通讯机构
院系归属：
心理学院
计算机学院
摘要：
Knowledge Base Question Answering (KBQA) is a hot research topic in natural language processing (NLP). The most challenging problem in KBQA is how to understand the semantic information of natural language questions and how to bridge the semantic gap between the natural language questions and the structured fact triples in knowledge base. This paper focuses on simple questions which can be answered by a single fact triple in knowledge base. We propose a topic enhanced deep structured semantic model for KBQA. The proposed method considers the task of KBQA as a matching problem between questions...
