A new method is proposed for automatically acquiring Fragments to understand fluent speech. The goal of this method is to generate a collection of Fragments, each representing a set of syntactically and semantically similar phrases. First, phrases frequently observed in the training set are selected as candidates. Each candidate phrase has three associated probability distributions of : following contexts, preceding contexts, and 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 to classify calls in AT&T's ``How May I Help You?'' task. The experimental results show that the average and maximum improvements in call-type classification performance of 2.2% and 2.8% are respectively achieved by introducing the Fragments.