Abstract
Document-based question answering (DBQA) is a sub-task of open-domain question answering, targeted at selecting the answer sentence(s) from the given documents for a question. In this paper, we propose a hybrid approach to select answer sentences, combining existing models via the rank SVM model. Specifically, we capture the inter-relationship between the question and answer sentences from three aspects: surface string similarity, deep semantic similarity and relevance based on information retrieval models. Our experiments show that an improved retrieval model out-performs other methods, including the deep learning models. And, applying a rank SVM model to combine all these features, we achieve 0.8120 in mean reciprocal rank (MRR) and 0.8111 in mean average precision (MAP) in the opening test.
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Acknowledgments
This paper is supported by the project of Natural Science Foundation of China (Grant Nos. 61272384, 61402134, and 61370170).
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Wu, F., Yang, M., Zhao, T., Han, Z., Zheng, D., Zhao, S. (2016). A Hybrid Approach to DBQA. In: Lin, CY., Xue, N., Zhao, D., Huang, X., Feng, Y. (eds) Natural Language Understanding and Intelligent Applications. ICCPOL NLPCC 2016 2016. Lecture Notes in Computer Science(), vol 10102. Springer, Cham. https://doi.org/10.1007/978-3-319-50496-4_87
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DOI: https://doi.org/10.1007/978-3-319-50496-4_87
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