6th SIGdial Workshop on Discourse and Dialogue
In this paper we investigate the use of machine learning techniques to classify a wide range of non-sentential utterance types in dialogue, a necessary first step in the interpretation of such fragments. We train different learners on a set of contextual features that can be extracted from PoS information. Our results achieve an 87% weighted f-score - a 25% improvement over a simple rule-based algorithm baseline.
Bibliographic reference. Fernández, Raquel / Ginzburg, Jonathan / Lappin, Shalom (2005): "Using machine learning for non-sentential utterance classification", In SIGdial6-2005, 77-86.