Learning Transformation Rules for Semantic Parsing (2004)
This paper presents an approach for inducing transformation rules that map natural-language sentences into a formal semantic representation language. The approach assumes a formal grammar for the target representation language and learns transformation rules that exploit the non-terminal symbols in this grammar. Patterns for the transformation rules are learned using an induction algorithm based on longest-common-subsequences previously developed for an information extraction system. Experimental results are presented on learning to map English coaching instructions for Robocup soccer into an existing formal language for coaching simulated robotic agents.
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unpublished. Unpublished Technical Report.
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Ruifang Ge Ph.D. Alumni grf [at] cs utexas edu
Rohit Kate Postdoctoral Alumni katerj [at] uwm edu
Rohit Kate Postdoctoral Alumni katerj [at] uwm edu
Raymond J. Mooney Faculty mooney [at] cs utexas edu
Yuk Wah Wong Ph.D. Alumni ywwong [at] cs utexas edu