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In this article we use well-known machine learning methods to tackle a novel task, namely the classification of non-sentential utterances (NSUs) in dialogue. We introduce a fine-grained taxonomy of NSU classes based on corpus work, and then report on the results of several machine learning experiments. First, we present a pilot study focussed on one of the NSU classes in the taxonomy-bare wh-phrases or 'sluices'-, and explore the task of disambiguating between the different readings that sluices can convey. We then extend the approach to classify the full range of NSU classes, obtaining results of around an 87% weighted F-score. Thus our experiments show that, for the taxonomy adopted, the task of identifying the right NSU class can be successfully learned, and hence provide a very encouraging basis for the more general enterprise of fully processing NSUs.(1) a. A: Who wants Beethoven music? B: Richard and James.

Semantic analysis of interaction and coordination in dialogue (SAICD), by the Lab(oratory of )Ex(cellence)-EFL (ANR/CGI), and by the Disfluency, Exclamations, and Laughter in Dialogue (DUEL) project within the projets franco-allemand en sciences humaines et sociales funded by the ANR and the DFG. We are grateful for comments to the participants in three courses we taught in which we presented a version of this material: Type Theory with Records for Natural Language Semantics, NASSLLI, Austin, Texas, 18th -22nd June, 2012; An introduction to semantics using type theory with records, ESSLLI, Opole, Poland, 13th -17th Aug, 2012; and Semantics using type theory with records, Gothenburg, 10th -12th June, 2013. We are grateful to Liz Coppock for comments on an earlier draft of this chapter. Finally, we would like to thank Chris Fox for his very penetrating and careful comments on the first submitted draft. A draft chapter for the Wiley-Blackwell Handbook of Contemporary Semanticssecond edition, edited by Shalom Lappin and Chris Fox. This draft formatted on 3rd April 2015.