Knowledge Base Question Answering Based on Deep Learning Models
This paper focuses on the task of knowledge-based question answering(KBQA).KBQA aims to match the questions with the structured semantics in knowledge base.In this paper,we propose a two-stage method.Firstly,we propose a topic entity extraction model(TEEM)to extract topic entities in questions,which does not rely on hand-crafted features or linguistic tools.We extract topic entities in questions with the TEEM and then search the knowledge triples which are related to the topic entities from the knowledge base as the candidate knowledge triples.Then,we apply Deep Structured Semantic Models based on convolutional neural network and bidirectional long short-term memory to match questions and predicates in the candidate knowledge triples.To obtain better training dataset,we use an iterative approach to retrieve the knowledge triples from the knowledge base.The evaluation result shows that our system achieves an AverageF1 measure of 79.57%on test dataset.
Zhiwen Xie Zhao Zeng Guangyou Zhou Tingting He
School of Computer,Central China Normal University,Wuhan 430079,China
国际会议
第五届自然语言处理与中文计算会议(NLPCC-ICCPOL2016)
昆明
英文
1-12
2016-12-02（万方平台首次上网日期，不代表论文的发表时间）