Transition Network Grammars for Syntactic Pattern Recognition.

摘要

The application of transition network grammars to syntactic pattern recognition is studied in this paper. The relation between basic transition networks and context-free grammars is demonstrated. Augmented transition networks can be used to represent context-sensitive, or even type 0 languages. Stochastic transition networks are defined and the parsing of languages represented by transition networks and stochastic transition networks investigated. Error-correcting parsing algorithms are proposed from the viewpoint of syntactic pattern recognition. The voice-chess grammar is used in an experiment to illustrate various parsing results.