In Proceedings of the Joint 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics (COLING/ACL-2006), pp. 913-920, Sydney, Australia, July 2006.
Paper ID: 191
Category: Natural Language Learning, Learning for Semantic Parsing, Advice-Taking Learners
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.
