  Over the past few years, question answering and information retrieval systems
have become widely used. These systems attempt to find the answer of the asked
questions from raw text sources. A component of these systems is Answer
Selection which selects the most relevant from candidate answers. Syntactic
similarities were mostly used to compute the similarity, but in recent works,
deep neural networks have been used, making a significant improvement in this
field. In this research, a model is proposed to select the most relevant
answers to the factoid question from the candidate answers. The proposed model
ranks the candidate answers in terms of semantic and syntactic similarity to
the question, using convolutional neural networks. In this research, Attention
mechanism and Sparse feature vector use the context-sensitive interactions
between questions and answer sentence. Wide convolution increases the
importance of the interrogative word. Pairwise ranking is used to learn
differentiable representations to distinguish positive and negative answers.
Our model indicates strong performance on the TrecQA Raw beating previous
state-of-the-art systems by 1.4% in MAP and 1.1% in MRR while using the
benefits of no additional syntactic parsers and external tools. The results
show that using context-sensitive interactions between question and answer
sentences can help to find the correct answer more accurately.
