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This paper presents automatic methods for the segmentation and classication of dialog acts (DA). In Verbmobil it is often sucient to recognize the sequence of DAs occurring during a dialog between the two partners. Since a turn can consist of one or more successive D As we conduct the classication of DAs in a two step procedure: First each turn has to be segmented into units which correspond to a DA and second the DA categories have to be identied. For the segmentation we use polygrams and multi{layer perceptrons, using prosodic features. The classication of DAs is done with semantic classication trees and polygrams.

We have developed a probabilistic modelling approach, which allows to consider diverse characteristic binding site properties to obtain more accurate representations of binding sites. These properties are modelled as random variables in Bayesian networks, which are capable of dealing with dependencies among binding site properties. Cross-validation on several datasets shows improvements in the false positive error rate and the significance (P-value) of true binding sites.

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