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Representation and Inference for Natural Language cover

Representation and Inference for Natural Language

A First Course in Computational Semantics

Patrick Blackburn and Johan Bos

Here is the first textbook wholly devoted to computational semantics, a lively subject still in its formative stage. A central question is how to represent meaning in ways usable by computers. Furthermore, can computers distinguish coherent from incoherent utterances, recognize new information in a sentence, or even draw inferences from a natural language passage?

Computer scientists, linguists, logicians, and indeed anyone curious about the role of meaning in human languages, will appreciate where questions such as the above lead this book. After the underlying theoretical issues are thoroughly introduced, complete implementations are presented of various fundamental techniques for computing semantic representations for fragments of natural language and for performing inference with the results. The reader who masters these techniques will be in a good position to appreciate, and critically assess, ongoing developments in computational semantics.

“An exciting combination of standard Montague techniques, modern approaches to underspecification, and the use of first-order theorem provers, all in a book than can be used by advanced undergraduates or graduate students.”
 –Robin Cooper, Gorg University

Patrick Blackburn is a Directeur de Recherche (Director of Research) at INRIA, France's national organization for research in computer science. Johan Bos is a senior researcher at the School of Informatics of the University of Edinburgh.

Contents

  • Preface
  • Introduction
  • 1 First-Order Logic
    • 1.1 First-Order Logic
    • 1.2 Three Inference Tasks
    • 1.3 A First-Order Model Checker
    • 1.4 First-Order Logic and Natural Language

  • 2 Lambda Calculus
    • 2.1 Compositionality
    • 2.2 Two Experiments
    • 2.3 The Lambda Calculus
    • 2.4 Implementing Lambda Calculus
    • 2.5 Grammar Engineering

  • 3 Underspecified Representation
    • 3.1 Scope Ambiguities
    • 3.2 Montague's Approach
    • 3.3 Storage Methods
    • 3.4 Hole Semantics

  • 4 Propositional Inference
    • 4.1 From Models to Proofs
    • 4.2 Propositional Tableaus
    • 4.3 Implementing Propositional Tableau
    • 4.4 Propositional Resolution
    • 4.5 Implementing Propositional Resolution
    • 4.6 Theoretical Remarks

  • 5 First-Order Inference
    • 5.1 A First-Order Tableau System
    • 5.2 Unification
    • 5.3 Free-Variable Tableaus
    • 5.4 Implementing Free-Variable Tableaus
    • 5.5 First-Order Resolution
    • 5.6 Implementing First-Order Resolution
    • 5.7 Off-the-Shelf Theorem Provers
    • 5.8 Model Building

  • 6 Putting It All Together
    • 6.1 Baby Curt
    • 6.2 Rugrat Curt
    • 6.3 Clever Curt
    • 6.4 Sensitive Curt
    • 6.5 Scrupulous Curt
    • 6.6 Knowledgeable Curt
    • 6.7 Helpful Curt

  • A Running the Software — FAQ
  • B Propositional Logic
  • C Automated Reasoning for First-Order Logic
  • D Notation
  • References
  • Author Index
  • Prolog Index
  • Subject Index

3/15/2005

ISBN (Paperback): 1575864967 (9781575864969)
ISBN (Electronic): 1575867737 (9781575867731)
ISBN (Cloth): 1575864959 (9781575864952)

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