Training Stochastic Grammars From Unlabelled Text Corpora

The paper describes various aspects and practicalities of applying the "Hidden Markov" approach to train parameters of regular and contextfree stochastic grammars, providing flexibility in the choice of syntactic categories and text domain.

Abstract

The paper describes various aspects and prac-ticalities of applying the "Hidden Markov " ap-proach to train parameters of regular and context-free stochastic grammars. The approach enables grammars to be trained from unlabelled text cor-pora, providing flexibility in the choice of syntac-tic categories and text domain. Part-of-speech tagging and parsing are discussed as applica-tions. Linguistic considerations can be used to de-velop constrained grammars, providing appropri-ate higher-order context for disamhiguation. Un-constrained grammars provide the opportunity to capture patterns that are not covered by a specific grammar. Experimental results are discussed for these alternatives.