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Over the past few years, question answering and information retrieval systemshave become widely used. These systems attempt to find the answer of the askedquestions from raw text sources. A component of these systems is AnswerSelection which selects the most relevant from candidate answers. Syntacticsimilarities were mostly used to compute the similarity, but in recent works,deep neural networks have been used, making a significant improvement in thisfield. In this research, a model is proposed to select the most relevantanswers to the factoid question from the candidate answers. The proposed modelranks the candidate answers in terms of semantic and syntactic similarity tothe question, using convolutional neural networks. In this research, Attentionmechanism and Sparse feature vector use the context-sensitive interactionsbetween questions and answer sentence. Wide convolution increases theimportance of the interrogative word. Pairwise ranking is used to learndifferentiable representations to distinguish positive and negative answers.Our model indicates strong performance on the TrecQA Raw beating previousstate-of-the-art systems by 1.4% in MAP and 1.1% in MRR while using thebenefits of no additional syntactic parsers and external tools. The resultsshow that using context-sensitive interactions between question and answersentences can help to find the correct answer more accurately.

Attention-based Pairwise Multi-Perspective Convolutional Neural Network for Answer Selection in Question Answering