Bayesian Natural Language Semantics and Pragmatics

1. Front Matter

   Pages i-xi

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2. Perspectives on Bayesian Natural Language Semantics and Pragmatics

   • Henk Zeevat

   Pages 1-24

3. Causal Bayesian Networks, Signalling Games and Implicature of ‘More Than n’

   • Anton Benz

   Pages 25-42

4. Measurement-Theoretic Foundations of Logic for Better Questions and Answers

   • Satoru Suzuki

   Pages 43-69

5. Conditionals, Conditional Probabilities, and Conditionalization

   • Stefan Kaufmann

   Pages 71-94

6. On the Probabilistic Notion of Causality: Models and Metalanguages

   • Christian Wurm

   Pages 95-116

7. Shannon Versus Chomsky: Brain Potentials and the Syntax-Semantics Distinction

   • Mathias Winther Madsen

   Pages 117-144

8. Orthogonality and Presuppositions: A Bayesian Perspective

   • Jacques Jayez

   Pages 145-178

9. Layered Meanings and Bayesian Argumentation: The Case of Exclusives

   • Grégoire Winterstein

   Pages 179-200

10. Variations on a Bayesian Theme: Comparing Bayesian Models of Referential Reasoning

    • Ciyang Qing, Michael Franke

    Pages 201-220

11. Towards a Probabilistic Semantics for Vague Adjectives

    • Peter R. Sutton

    Pages 221-246

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

• ILLC, University of Amsterdam, Amsterdam, The Netherlands

  Henk Zeevat

• Fraunhofer Institute for Communication, Information Processing and Ergonomics FKIE, Wachtberg, Germany

  Hans-Christian Schmitz

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