Abstract

This paper focuses on the learning of multi-word lexical units, or phrases, and how to model them within the variable n-gram framework. We introduce the notion of contextdependent phrases and suggest an algorithm for unsupervised learning of phrases. Also, we propose an approach to integrate a phrase grammar and a variable n-gram without the need of explicitly handling multi-word lexical items. The combined variable n-gram phrase grammar improves recognition accuracy on the Switchboard corpus over both the baseline trigram and using a variable n-gram alone. 1. INTRODUCTION Although words in English are reasonable lexical units for language modeling, there are many cases that longer lexical units may be more appropriate. Frequently used word sequences, such as I mean or you know, are so common in conversational speech that they may be effectively used by the speaker as a single lexical item. We call these multiword units "phrases". There are several ways of treating a multi-word sequ...

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