© 2015

Bayesian Natural Language Semantics and Pragmatics

  • Henk Zeevat
  • Hans-Christian Schmitz

Part of the Language, Cognition, and Mind book series (LCAM, volume 2)

About this book


The contributions in this volume focus on the Bayesian interpretation of natural languages, which is widely used in areas of artificial intelligence, cognitive science, and computational linguistics. This is the first volume to take up topics in Bayesian Natural Language Interpretation and make proposals based on information theory, probability theory, and related fields. The methodologies offered here extend to the target semantic and pragmatic analyses of computational natural language interpretation.


Bayesian approaches to natural language semantics and pragmatics are based on methods from signal processing and the causal Bayesian models pioneered by especially Pearl. In signal processing, the Bayesian method finds the most probable interpretation by finding the one that maximizes the product of the prior probability and the likelihood of the interpretation. It thus stresses the importance of a production model for interpretation as in Grice’s contributions to pragmatics or in interpretation by abduction.


Bayesian Pragmatics Bayesian interpretation Bayesian natural language semantics Bayesian networks Signal processing and probability Syntax-semantics distinction analysis of counter factuals causal Bayesian models computational logic computational natural language interpretation logic and semantics natural language processing natural language semantics stochastic methods for language interpretation

Editors and affiliations

  • Henk Zeevat
    • 1
  • Hans-Christian Schmitz
    • 2
  1. 1.ILLCUniversity of AmsterdamAmsterdamThe Netherlands
  2. 2.Fraunhofer Institute for Communication, Information Processing and Ergonomics FKIEWachtbergGermany

Bibliographic information