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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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
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