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

Edward C. Kaiser, Michael Johnston, Peter A. Heeman

Research output: Contribution to journalConference article

14 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)629-632
Number of pages4
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2
StatePublished - Jan 1 1999
EventProceedings of the 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP-99) - Phoenix, AZ, USA
Duration: Mar 15 1999Mar 19 1999

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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