Pragmatics and Computational Linguistics [for Handbook of Pragmatics]

These days there’s a computational version of everything. Computational biology, computational musicology, computational archaeology, etc, etc. Even movies are going digital. This chapter, as you might have guessed by now, thus explores the computational side of pragmatics. Computational pragmatics might be defined as the computational study of the relation between utterances and context. Like other kinds of pragmatics, this means that computational pragmatics is concerned with indexicality, with the relation between utterances and action, with the relation between utterances and discourse, and with the relationship between utterances and the place, time, and environmental context of their being uttered. As Bunt and Black (2000) point out, computational pragmatics, like pragmatics in general, is especially concerned with inference. Four core inferential problems in pragmatics have received the most attention in the computational community: reference resolution, the interpretation and generation of speech acts, the interpretation and generation of discourse structure, and the problem of abduction. Each of these four problems can be cast as an inference task, one of somehow filling in information that isn’t actually present in the utterance at hand. Two of these tasks are addressed in other chapters of this volume; abduction in Chapter X, and discourse structure and coherence in Chapter Y. I have therefore chosen the interpretation of speech acts as the topic of this chapter. Speech act interpretation, a classic pragmatic problem, is a good choice for this overview chapter for many reasons. First, the early computational work drew very strongly from the linguistic literature of the period. This enables us to closely compare the ways that computational linguistic and non-computational linguistic approaches differ in their methodology Second, there are two distinct computational paradigms in speech act interpretation: a logic-based approach and a probabilistic approach. I see these two approaches as good vehicles for motivating the