Abstract
Machine comprehension of various forms of semantically similar questions with same or similar answers has been an ongoing challenge. Especially in many industrial domains with limited set of questions, it is hard to identify proper semantic match for a newly asked question having the same answer but presented in different lexical form. This paper proposes a linguistically motivated taxonomy for English questions and an effective approach for question matching by combining deep learning models for question representations with general taxonomy based features. Experiments performed on short datasets demonstrate the effectiveness of the proposed approach as better matching classification was observed by coupling the standard distributional features with knowledge-based methods.
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Maitra, A. et al. (2018). Semantic Question Matching in Data Constrained Environment. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds) Text, Speech, and Dialogue. TSD 2018. Lecture Notes in Computer Science(), vol 11107. Springer, Cham. https://doi.org/10.1007/978-3-030-00794-2_29
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