Combining Deep Learning with Information Retrieval for Question Answering

摘要：

　　This paper presents a system which learns to answer single-relation questions on a broad range of topics from a knowledge base using a threelayered learning system.Our system first learning a Topic Phrase Detecting model based on a phrase-entities dictionary to detect which phrase is the topic phrase of the question.The second layer of the system learning several answer ranking models.The last layer re-ranking the scores from the output of the second layer and return the highest scored answer.Both convolutional neural networks(CNN)and information retrieval(IR)models are included in this models.Training our system using pairs of questions and structured representations of their answers,yields competitive results on the NLPCC 2016 KBQA share task.

关键词： Question Answering Deep Learning Information Retrieval Knowledge Base

作者: Fengyu Yang Liang Gan Aiping Li Dongchuan Huang Xiaohui Chou Hongmei Liu

作者单位: Department of Computer,National University of Defense Technology,Changsha

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）