Abstract
Domain-specific question answering over knowledge base generates an answer for a natural language question based on a domain-specific knowledge base. But it often faces a lack of domain training resources such as question answer pairs or even questions. To address this issue, we propose a domain adaptive method to construct a domain-specific question answering system using easily accessible open domain questions. Specifically, generalization features are proposed to represent questions, which can categorize questions according to their syntactic forms. The features are adaptive from open domain into domain by terminology transfer. And a fuzzy matching method based on character vector are used to do knowledge base retrieving. Extensive experiments on real datasets demonstrate the effectiveness of the proposed method.
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Acknowledgements
The work is supported by NSFC key projects (U1736204, 61533018, 61661146007), Ministry of Education and China Mobile Joint Fund (MCM20170301), a research fund supported by Alibaba Group, and THUNUS NExT Co-Lab.
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Yang, Y. et al. (2019). Domain Adaptive Question Answering over Knowledge Base. In: Tang, J., Kan, MY., Zhao, D., Li, S., Zan, H. (eds) Natural Language Processing and Chinese Computing. NLPCC 2019. Lecture Notes in Computer Science(), vol 11839. Springer, Cham. https://doi.org/10.1007/978-3-030-32236-6_15
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DOI: https://doi.org/10.1007/978-3-030-32236-6_15
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