This paper investigates the usefulness of prosodic features in classifying rhetorical relations between utterances in meeting recordings. Five rhetorical relations of contrast, elaboration, summary, question and cause are explored. Three training methods - supervised, unsupervised, and combined - are compared, and classification is carried out using support vector machines. The results of this pilot study are encouraging but mixed, with pairwise classification achieving an average of 68% accuracy in discerning between relation pairs using only prosodic features, but multi-class classification performing only slightly better than chance.
|Title of host publication||Proceedings of the Analyzing Conversations in Text and Speech (ACTS), Proceedings of the Workshop|
|Subtitle of host publication||HLT/NAACL 06|
|Number of pages||8|
|Publication status||Published - 1 Jun 2006|