Abstract
Compositional question answering first maps natural language sentences into meaning representations, then a meaning interpreter is used to evaluate the corresponding answers against a database. A novel approach is proposed in this paper which involves a concept base with rich hierarchical information. A new meaning representation form is introduced correspondingly to match the hierarchical concept base. A set of constructions which encode the correspondence of concept sequences and their meaning representations are used for parsing. The experimental results show that the proposed semantic parser performs favorably in terms of both accuracy and generalization performance compared to existing semantic parsers.
Xihong Wu—IEEE Senior member.
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Acknowledgments
The research was partially supported by the National Basic Research Program of China (973 Program) under grant 2013CB329304, the Major Project of National Social Science Foundation of China under grant 12&ZD119, and the National Natural Science Foundation of China under grant 61121002.
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Gao, Y., Hong, C., Wu, X. (2015). Semantic Parsing Using Hierarchical Concept Base. In: He, X., et al. Intelligence Science and Big Data Engineering. Big Data and Machine Learning Techniques. IScIDE 2015. Lecture Notes in Computer Science(), vol 9243. Springer, Cham. https://doi.org/10.1007/978-3-319-23862-3_56
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