PROFER: predictive, robust finite-state parsing for spoken language

Abstract

The natural language processing component of a speech understanding system is commonly a robust, semantic parser, implemented as either a chart-based transition network, or as a generalized left-right (GLR) parser. In contrast, we are developing a robust, semantic parser that is a single, predictive finite-state machine. Our approach is motivated by our belief that such a finite-state parser can ultimately provide an efficient vehicle for tightly integrating higher-level linguistic knowledge into speech recognition. We report on our development of this parser, with an example of its use, and a description of how it compares to both finite-state predictors and chart-based semantic parsers, while combining the elements of both.