Grammar Fragment acquisition using syntactic and semantic clustering

A new method for automatically acquiring Fragments for understanding fluent speech is proposed. The goal of this method is to generate a collection of Fragments, each representing a set of syntactically and semantically similar phrases. First, phrases observed frequently in the training set are selected as candidates. Each candidate phrase has three associated probability distributions: of following contexts, of preceding contexts, and of associated semantic actions. The similarity between candidate phrases is measured by applying the Kullback-Leibler distance to these three probability distributions. Candidate phrases that are close in all three distances are clustered into a Fragment. Salient sequences of these Fragments are then automatically acquired, and exploited by a spoken language understanding module to classify calls in AT&T's ``How may I help you?{''} task. These Fragments allow us to generalize unobserved phrases. For instance, they detected 246 phrases in the test-set that were not present in the training-set. This result shows that unseen phrases can be automatically discovered by our new method. Experimental results show that 2.8% of the improvement in call-type classification performance was achieved by introducing these Fragments. (C) 1999 Elsevier Science B.V. All rights reserved.