Perspectives on Bayesian Natural Language Semantics and Pragmatics

Abstract

Bayesian interpretation is a technique in signal processing and its application to natural language semantics and pragmatics (BNLSP from here on and BNLI if there is no particular emphasis on semantics and pragmatics) is basically an engineering decision. It is a cognitive science hypothesis that humans emulate BNLSP. That hypothesis offers a new perspective on the logic of interpretation and the recognition of other people’s intentions in inter-human communication. The hypothesis also has the potential of changing linguistic theory, because the mapping from meaning to form becomes the central one to capture in accounts of phonology, morphology and syntax. Semantics is essentially read off from this mapping and pragmatics is essentially reduced to probability maximation within Grice’s intention recognition. Finally, the stochastic models used can be causal, thus incorporating new ideas on the analysis of causality using Bayesian nets. The paper explores and connects these different ways of being committed to BNLSP.