Training Stochastic Grammars From Unlabelled Text Corpora
Julian M. Kupiec, John T. Maxwell · 1992
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.