%0 Journal Article %A FENG Wenzheng %A TANG Jie %T A Ranking Model for Answer Selection with Deep Matching Features %D 2019 %R %J Journal of Chinese Information Processing %P 118-124 %V 33 %N 1 %X Answer Selection is one of the key tasks in question answering system. Its main purpose is to rank the candidate answers according to the similarity between the questions and the candidate answers and select the more relevant answers to users. It can be seen as a text pair matching problem. In this paper, we use the deeplearning model such as word embedding, bidirectional LSTM, 2D neural network and so on to extract the semantic matching features for question-answer pairs, and incorperate these into a ranking model together with traditonal NLP features. The experiments on the Qatar Living community question answering data show that the answer selection model with deep matching features is about 5% higher than only using traditional features on the MAP values. %U http://jcip.cipsc.org.cn/EN/abstract/article_2705.shtml