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Chapter Summary

This chapter presents a linear algorithm which produces interpretations based on a Bayesian interpretation scheme. The algorithm constructs the most probable interpretation incrementally, produces pragmatically enriched interpretations, and is highly robust. It uses knowledge resources that human speakers have at their disposal and which may be obtainable from the huge corpora now available to computational linguists. The chapter explains how the pragmatic effects are obtained. It also sketches the possibility of integrating information obtained from other perception with natural language interpretation. Compared to interpretation by abduction as proposed by Hobbs et al., the chapter presents a more radical but still abductive proposal. Where Hobbs takes on board a full-fledged Aristotelian grammar, the proposal of the chapter uses abduction all the way. The restriction to a context is essential. One could define syntactically well-formed utterances and turns by existentially quantifying over contexts and interpretations.

Keywords: Aristotelian grammar; Bayesian interpretation; computational linguists; Hobbs; linear algorithm; pragmatic effects



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