Combination of CFG and N-gram Modeling in Semantic Grammar Learning

Ye-Yi Wang and Alex Acero

Abstract

SGStudio is a grammar authoring tool that eases semantic grammar development. It is capable of integrating different information sources and learning from annotated examples to induct CFG rules. In this paper, we investigate a modification to its underlying model by replacing CFG rules with n-gram statistical models. The new model is a composite of HMM and CFG. The advantages of the new model include its built-in robust feature and its scalability to an n-gram classifier when the understanding does not involve slot filling. We devised a decoder for the model. Preliminary results show that the new model achieved 32% error reduction in high resolution understanding.

Details

Publication typeInproceedings
Published inEurospeech 2003
Pages2809-2812
AddressGeneva, Switzerland
PublisherInternational Speech Communication Association
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