Segmentation of spoken discourse into distinct conversational activities has been applied to broadcast news, meetings, monologs, and two-party dialogs. This paper considers the aspectual properties of discourse segments, meaning how they transpire in time. Classifiers were constructed to distinguish between segment boundaries and non-boundaries, where the sizes of utterance spans to represent data instances were varied, and the locations of segment boundaries relative to these instances. Classifier performance was better for representations that included the end of one discourse segment combined with the beginning of the next. In addition, classification accuracy was better for segments in which speakers accomplish goals with distinctive start and end points.
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