Abstract
Document-based Question Answering system, which needs to match semantically the short text pairs, has gradually become an important topic in the fields of natural language processing and information retrieval. Question Answering system based on English corpus has developed rapidly with the utilization of the deep learning technology, whereas an effective Chinese-customized system needs to be paid more attention. Thus, we explore a Question Answering system which is characterized in Chinese for the QA task of NLPCC. In our approach, the ordered sequential information of text and deep matching of semantics of Chinese textual pairs have been captured by our count-based traditional methods and embedding-based neural network. The ensemble strategy has achieved a good performance which is much stronger than the provided baselines.
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Acknowledgements
The work presented in this paper is sponsored in part by the Chinese National Program on Key Basic Research Project (973 Program, grant Nos. 2013CB329304, 2014CB744604), the Chinese 863 Program (grant No. 2015AA015403), the Natural Science Foundation of China (grant Nos. 61402324, 61272265), and the Research Fund for the Doctoral Program of Higher Education of China (grant No. 20130032120044).
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Wang, B. et al. (2016). A Chinese Question Answering Approach Integrating Count-Based and Embedding-Based Features. 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_88
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DOI: https://doi.org/10.1007/978-3-319-50496-4_88
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