A Bidirectional LSTM Model for Question Title and Body Analysis in Question Answering
Yuanping Nie, Chao An, Jiuming Huang, Yan Zhou, Yi Han · 2016
Community Question Answering(CQA) services become popular recently. In most existing CQA service, the question posted by real user is usually consist by two parts: question title and body. And there is a semantic relation between them. It is necessary to analyze the relation between title and body. In this paper, we propose a deep neural network based method to analyze and quantify the relation between question title and body. The proposed method employs Bidirectional LSTM to read the title and body separately. And finally our model outputs a relevance score to measure the semantic relation between the question title and body. We evaluate our model on the Yahoo!Answers dataset and the experimental results show our method is effective.