We present a new approach for mapping natural language sentences to their formal meaning representations using string-kernel-based classifiers. Our system learns these classifiers for every production in the formal language grammar. Meaning representations for novel natural language sentences are obtained by finding the most probable semantic parse using these string classifiers. Our experiments on two real-world data sets show that this approach compares favorably to other existing systems and is particularly robust to noise.

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In ACL 2006: Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the ACL, pp. 913-920, Morristown, NJ, USA 2006. Association for Computational Linguistics.

@InProceedings{kate:acl06, title={Using String-Kernels for Learning Semantic Parsers}, author={Rohit J. Kate and Raymond J. Mooney}, booktitle={ACL 2006: Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the ACL}, address={Morristown, NJ, USA}, publisher={Association for Computational Linguistics}, pages={913-920}, url="http://www.cs.utexas.edu/users/ai-lab?kate:acl06", year={2006} }

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