Abstract | The literature contains a wealth of theoretical and empirical analyses of discourse marker functions in human communication. Some of these studies address the phenomenon that discourse markers are often multifunctional in a given context, but do not study this in systematic and formal ways. In this paper we show that the use of multiple dimensions in distinguishing and annotating semantic units supports a more accurate analysis of the meaning of discourse markers. We present an empirically-based analysis of the semantic functions of discourse markers in dialogue and show that the multiple functions that a discourse marker may have, are automatically recognisable from utterance surface features using machine learning techniques.
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