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Learning for Semantic Parsing

  • Conference paper
Computational Linguistics and Intelligent Text Processing (CICLing 2007)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4394))

Abstract

Semantic parsing is the task of mapping a natural language sentence into a complete, formal meaning representation. Over the past decade, we have developed a number of machine learning methods for inducing semantic parsers by training on a corpus of sentences paired with their meaning representations in a specified formal language. We have demonstrated these methods on the automated construction of natural-language interfaces to databases and robot command languages. This paper reviews our prior work on this topic and discusses directions for future research.

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Authors and Affiliations

  1. Department of Computer Sciences, University of Texas at Austin, 1 University Station C0500, Austin, TX 78712-0233, USA

    Raymond J. Mooney

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  1. Raymond J. Mooney
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Alexander Gelbukh

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Mooney, R.J. (2007). Learning for Semantic Parsing. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2007. Lecture Notes in Computer Science, vol 4394. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70939-8_28

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  • DOI: https://doi.org/10.1007/978-3-540-70939-8_28

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-70938-1

  • Online ISBN: 978-3-540-70939-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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