An Open Domain Topic Prediction Model for Answer Selection

摘要：

　　We present an open domain topic prediction model for the answer selection task.Different from previous unsupervised topic modeling methods,we automatically extract high quality and large scale hsentence,topici pairs from Wikipedia as labeled data,and train an open domain topic prediction model based on convolutional neural network,which can predict the most possible topics for each given input sentence.To verify the usefulness of our proposed approach,we add the topic prediction model into an end-to-end open domain question answering system and evaluate it on the answer selection task,and improvements are obtained on both WikiQA and QASent datasets.

关键词： Answer selection Question answering Topic prediction

作者: Zhao Yan Nan Duan Ming Zhou Zhoujun Li Jianshe Zhou

作者单位: Beihang University,No.37 Xueyuan Road,Beijing 100191,China Microsoft Research Asia,Beijing,China BAICIT,Capital Normal University

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: 1-12

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）