Using Machine Learning for Non-Sentential Utterance Classification

Abstract
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.
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    S. Russell (1991). Inductive Learning by Machines. Philosophical Studies 64 (October):37-64.
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