Using Machine Learning for Non-Sentential Utterance Classification
Raquel Fernández, Jonathan Ginzburg, Shalom Lappin · 2005
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.