Abstract
Semantic parsing which maps a natural language sentence into its meaning representation is considered in this paper. A novel approach which involves construction categorization is proposed. Constructions encode the correspondences of concept sequences and their meaning representations. They are categorized using the syntactic relations included in a predefined hierarchical concept base. The semantic parser construct the meaning representations for the input sentences based on these constructions. Evaluations on a benchmark dataset, GeoQuery, demonstrate that the proposed semantic parser provides favorable accuracy, as well as the generalization performance.
X.Wu—IEEE Senior member.
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References
Cai, Q., Yates, A.: Semantic parsing freebase: towards open-domain semantic parsing. In: Proceedings of the Joint Conference on Lexical and Computational Semantics, vol. 30, pp. 328 (2013)
Kwiatkowski, T., Zettlemoyer, L., Goldwater, S., and Steedman, M.: Lexical generalization in CCG grammar induction for semantic parsing. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 1512–1523 (2011)
Kwiatkowski, T., Choi, E., Artzi, Y., and Zettlemoyer, L.: Scaling semantic parsers with on-the-fly ontology matching. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. Seattle, Washington (2013)
Wang, A., Kwiatkowski, T. and Zettlemoyer, L.: Morpho-syntactic lexical generalization for CCG semantic parsing. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP) (2014)
Ge, R., Mooney, R.J.: A statistical semantic parser that integrates syntax and semantics. In: Proceedings of the Ninth Conference on Computational Natural Language Learning, Association for Computational Linguistics, pp. 9–16 (2005)
Ge, R., Mooney, R. J.: Discriminative reranking for semantic parsing. In: Proceedings of the COLING/ACL on Main Conference Poster Sessions, Association for Computational Linguistics, pp. 263–270 (2006)
Ge, R., Mooney, R.J.: Learning a compositional semantic parser using an existing syntactic parser. In: Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing. Association for Computational Linguistics, vol. 2, pp. 611–619 (2009)
Ge, R.: Learning for semantic parsing using statistical syntactic parsing techniques. Ph.D. thesis, University of Texas at Austin (2010)
Wong, Y.W., and Mooney, R.J.: Learning for semantic parsing with statistical machine translation. In: Proceedings of HLT/NAACL 2006, pp. 439–446, New York City (2006)
Wong, Y.W. and Mooney, R.J.: Learning synchronous grammars for semantic parsing with lambda calculus. In: Association for Computational Linguistics (ACL), pp. 960–967, Prague, Czech Republic (2007)
Andreas, J., Vlachos, A., and Clark, S.: Semantic parsing as machine translation. In: Proceedings of the Conference of the Association for Computational Linguistics (ACL), pp. 47–52 (2013)
Krishnamurthy, J., Mitchell, T.: Weakly supervised training of semantic parsers. In: Proceedings of the Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp. 754–765 (2012)
Berant, J., Chou, A., Frostig, R., Liang, P.: Semantic parsing on freebase from question-answer pairs. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1533–1544 (2013)
Zelle, M., Mooney, R.J.: Learning to parse database queries using inductive logic proramming. In: Association for the Advancement of Artificial Intelligence (AAAI). MIT Press, Cambridge (1996)
Earley, J.: An efficient context-free parsing algorithm. Commun. Assoc. Comput. Mach. 6(8), 451–455 (1970)
Liang, P., Jordan, M.I., Klein, D.: Learning dependency-based compositional semantics. Comput. Linguist. 39(2), 389–446 (2013)
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 Construction Categorization. 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_57
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