Personalization of Word-Phrase-Entity Language Models

We continue our investigations of Word-Phrase-Entity (WPE) Language Models that unify words, phrases and classes, such as named entities, into a single probabilistic framework for the purpose of language modeling. In the present study we show how WPE LMs can be adapted to work in a personalized scenario where class definitions change from user to user or even from utterance to utterance. Compared to traditional classbased LMs in various conditions, WPE LMs exhibited comparable or better modeling potential without requiring pre-tagged training material. We also significantly scaled the experimental setup by widening the target domain, amplifying the amount of training material and increasing the number of classes.