Computational Models of Referring

A Study in Cognitive Science
Overview

To communicate, speakers need to make it clear what they are talking about. The act of referring, which anchors words to things, is a fundamental aspect of language. In this book, Kees van Deemter shows that computational models of reference offer attractive tools for capturing the complexity of referring. Indeed, the models van Deemter presents cover many issues beyond the basic idea of referring to an object, including reference to sets, approximate descriptions, descriptions produced under uncertainty concerning the hearer’s knowledge, and descriptions that aim to inform or influence the hearer.

The book, which can be read as a case study in cognitive science, draws on perspectives from across the cognitive sciences, including philosophy, experimental psychology, formal logic, and computer science. Van Deemter advocates a combination of computational modeling and careful experimentation as the preferred method for expanding these insights. He then shows this method in action, covering a range of algorithms and a variety of methods for testing them. He shows that the method allows us to model logically complicated referring expressions, and demonstrates how we can gain an understanding of reference in situations where the speaker’s knowledge is difficult to assess or where the referent resists exact definition. Finally, he proposes a program of research that addresses the open questions that remain in this area, arguing that this program can significantly enhance our understanding of human communication.

Table of Contents

  1. Preface
  2. 1. Aims and Scope of This Book
  3. 2. Theories of Reference
  4. 3. The Psychology of Reference Production
  5. 4. Getting Computers to Refer
  6. 5. Testing REG Algorithms: The TUNA Experiment
  7. 6. Probabilistic and Other Alternatives to the Classic REG Algorithms
  8. 7. First Extension: Using Proper Names
  9. 8. Second Extension: Referring to Sets
  10. 9. Third Extension: Using Gradable Properties
  11. 10. Fourth Extension: Exploiting Modern Knowledge Representation
  12. 11. The Question of Referability
  13. 12. First Challenge: Large Domains
  14. 13. Second Challenge: Breakdown of Common Knowledge
  15. 14. Third Challenge: Approximate Reference
  16. 15. Fourth Challenge: Going Beyond Identification
  17. Summary of Part IV: Complexities of Information Sharing
  18. 16. Epilogue
  19. Frequently Occurring Terms and Abbreviations
  20. Bibliography
  21. Index