A Hybrid Approach to DBQA

摘要：

　　Document-based question answering(DBQA)is a sub-task of opendomain question answering,targeted at selecting the answer sentence(s)from the given documents for a question.In this paper,we propose a hybrid approach to select answer sentences,combining existing models via the rank SVM model.Specifically,we capture the inter-relationship between the question and answer sentences from three aspects: surface string similarity,deep semantic similarity and relevance based on information retrieval models.Our experiments show that an improved retrieval model out-performs other methods,including the deep learning models.And,applying a rank SVM model to combine all these features,we achieve 0.8120 in mean reciprocal rank(MRR)and 0.8111 in mean average precision(MAP)in the opening test.

关键词： QA String Similarity Information Retrieval Deep Learning Rank SVM Hybrid Approach

作者: Fangying Wu Muyun Yang Tiejun Zhao Zhongyuan Han Dequan Zheng Shanshan Zhao

作者单位: Harbin Institute of Technology,Harbin,China

会议类型: 国际会议

会议名称: 第五届自然语言处理与中文计算会议(NLPCC-ICCPOL2016)

会议地点: 昆明

会议语种:英文

页码: 1-8

在线出版日期: 2016-12-02（万方平台首次上网日期，不代表论文的发表时间）