This paper takes a classical machine learning approach to the task of dialogue act segmentation. A thorough empirical evaluation of features, both used in other studies as well as new ones, is performed. An explorative study to the e ectiveness of di erent classi cation methods is done by looking at 29 di erent classi ers implemented in WEKA. The output of the developed classi er is examined closely and points of possible improvement are given.
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