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. .
