Automatic Acquisition of Phrase Grammars for Stochastic Language Modeling

Phrase based language models have been recognized to have an advantage over word based language models since they allow us to capture long span ning dependencies Class based language models have been used to improve model generalization and overcome problems with data sparseness In this pa per we present a novel approach for combining the phrase acquisition with class construction process to automatically acquire phrase grammar fragments from a given corpus The phrase grammar learning is decomposed into two sub problems namely the phrase acquisition and feature selection The phrase acquisition is based on entropy minimization and the feature selection is driven by the entropy reduction principle We further demonstrate that the phrase grammar based n gram language model signi cantly outperforms a phrase based n gram language model in an end to end evaluation of a spoken language application