2019/09/03 by Jamshid Mozafari, Mozafari, Jamshid, Mohammad Ali Nematbakhsh +3
Computer Science · #Computation and Language (cs.CL) #Expert finding and Q&A systems #FOS: Computer and information sciences #Natural Language Processing Techniques #Topic Modeling #cs.CL
paper · pdf · doi:10.48550/arxiv.1909.01059
13 pages, 11 figures, journal
openalex publication_date 2019/09/03 · arxiv created 2019/11/11 · arxiv updated 2019/11/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.