In this paper we present a multidimensional approach to utterance segmentation
and automatic dialogue act classification. We show that the use of multiple 
dimensions in distinguishing and annotating units not only supports a more 
accurate analysis of human communication, but can also help to solve some 
notorious problems concerning the segmentation of dialogue into functional 
units. 

We introduce the use of per-dimension segmentation for dialogue act taxonomies
that feature multi-functionality and show that better classification results 
are obtained when using a separate segmentation for each dimension than when 
using one segmentation that fits all dimensions. Three machine learning 
techniques are applied and compared on the task of automatic classification 
of multiple communicative functions of utterances. The results are encouraging 
and indicate that communicative functions in important dimensions are easy 
machine-learnable.