Learning Semantic Parsers: An Important But Under-Studied Problem (2004)
Computational systems that learn to transform natural-language sentences into semantic representations have important practical applications in building natural-language interfaces. They can also provide insight into important issues in human language acquisition. However, within AI, computational linguistics, and machine learning, there has been relatively little research on developing systems that learn such semantic parsers. This paper briefly reviews our own work in this area and presents semantic-parser acquistion as an important challenge problem for AI.
In Papers from the AAAI 2004 Spring Symposium on Language Learning: An Interdisciplinary Perspective, pp. 39--44, Stanford, CA, March 2004.

Raymond J. Mooney Faculty mooney [at] cs utexas edu