Semantic parsing involves deep semantic analysis that maps natural language sentences to their formal executable meaning representations. This is a challenging problem and is critical for developing user-friendly natural language interfaces to computing systems. Most of the research in natural language understanding, however, has mainly focused on shallow semantic analysis like case-role analysis or word sense disambiguation. The existing work in semantic parsing either lack the robustness of statistical methods or are applicable only to simple domains where semantic analysis is equivalent to filling a single semantic frame.

In this proposal, we present a new approach to semantic parsing based on string-kernel-based classification. Our system takes natural language sentences paired with their formal meaning representations as training data. For every production in the formal language grammar, a Support-Vector Machine (SVM) classifier is trained using string similarity as the kernel. Each classifier then gives the probability of the production covering any given natural language string of words. These classifiers are further refined using EM-type iterations based on their performance on the training data. Meaning representations for novel natural language sentences are obtained by finding the most probable semantic parse using these classifiers. Our experiments on two real-world data sets that have deep meaning representations show that this approach compares favorably to other existing systems in terms of accuracy and coverage.

For future work, we propose to extend this approach so that it will also exploit the knowledge of natural language syntax by using the existing syntactic parsers. We also intend to broaden the scope of application domains, for example, domains where the sentences are noisy as typical in speech, or domains where corpora available for training do not have natural language sentences aligned with their unique meaning representations. We aim to test our system on the task of complex relation extraction as well. Finally, we also plan to investigate ways to combine our semantic parser with some recently developed semantic parsers to form committees in order to get the best overall performance.

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unpublished. Doctoral Dissertation Proposal, University of Texas at Austin.

@Unpublished{kate:proposal05, title={A Kernel-based Approach to Learning Semantic Parsers}, author={Rohit J. Kate}, note={Doctoral Dissertation Proposal, University of Texas at Austin}, url="http://www.cs.utexas.edu/users/ai-lab?kate:proposal05", year={2005} }

Slides (PPT)