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We present a new approach for mapping natural language sentences to their formal meaning representations using stringkernel-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 realworld data sets show that this approach compares favorably to other existing systems and is particularly robust to noise. . | Using String-Kernels for Learning Semantic Parsers Rohit J. Kate Department of Computer Sciences The University of Texas at Austin 1 University Station C0500 Austin TX 78712-0233 USA rjkate@cs.utexas.edu Raymond J. Mooney Department of Computer Sciences The University of Texas at Austin 1 University Station C0500 Austin TX 78712-0233 USA mooney@cs.utexas.edu Abstract We present a new approach for mapping natural language sentences to their formal meaning representations using stringkernel-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. 1 Introduction Computational systems that learn to transform natural language sentences into formal meaning representations have important practical applications in enabling user-friendly natural language communication with computers. However most of the research in natural language processing NLP has been focused on lower-level tasks like syntactic parsing word-sense disambiguation information extraction etc. In this paper we have considered the important task of doing deep semantic parsing to map sentences into their computer-executable meaning representations. Previous work on learning semantic parsers either employ rule-based algorithms Tang and Mooney 2001 Kate et al. 2005 or use statistical feature-based methods Ge and Mooney 2005 Zettlemoyer and Collins 2005 Wong and Mooney 2006 . In this paper we present a novel kernel-based statistical method for learning semantic parsers. Kernel methods Cristianini and Shawe-Taylor 2000 are particularly suitable for semantic parsing because it involves mapping phrases of natural language NL sentences to semantic concepts in a meaning .
