Learning for Semantic Parsing of Natural Language

Ray Mooney

University of Texas-Austin

Semantic parsing is the task of mapping a natural-language sentence into a detailed formal representation of its meaning. This talk presents a summary of our research on learning semantic parsers from corpora of sentences annotated with formal representations. Our original work employed inductive-logic programming methods to learn deterministic symbolic parsers, our more recent work has applied current techniques from statistical syntactic parsing, machine translation, and support vector machines using string kernels to learn more robust semantic parsers. We present results on learning to interpret natural language database queries and robot commands (Robocup coaching instructions). Joint work with Ruifang Ge, Yuk Wah Wong, and Rohit Kate

Bio: Raymond J. Mooney is a Professor in the Department of
Computer Sciences at the University of Texas at Austin. He received
his Ph.D. in 1988 from the University of Illinois at
Urbana/Champaign. He is an author of over 100 published research
papers, primarily in the area of machine learning. He is program
co-chair for the 2006 National Conference on Artificial Intelligence,
a recent general chair of the 2005 Human Language Technology
Conference and Conference on Empirical Methods in Natural Language
Processing, a former co-chair of the 1990 International Conference on
Machine Learning, a former editor of the Machine Learning journal, and
a Fellow of the American Association for Artificial Intelligence.  His
recent research has focused on learning for natural-language
processing, text mining, statistical relational learning,
semi-supervised learning, bioinformatics, and autonomic computing.

Deepak Ramachandran

Last modified: Tue Apr 18 11:36:27 CDT 2006