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Data-Oriented Parsing cover

Data-Oriented Parsing

edited by Rens Bod, Remko Scha, and Khalil Sima'an

Data-Oriented Parsing (DOP) is one of the leading paradigms in Statistical Natural Language Processing. In this volume, a collection of computational linguists offer a state-of-the-art overview of DOP, suitable for students and researchers in natural language processing and speech recognition as well as for computational linguistics. This handbook begins with the theoretical background of DOP and introduces the algorithms used in DOP as well as in other probabilistic grammar models. After surveying extensions to the basic DOP model, the volume concludes with close study of the applications that use DOP as a backbone: speech understanding, machine translation, and language learning.

Rens Bod, Remko Scha and Khalil Sima'an are senior scientists at the the Institute for Logic, Language and Computation at the University of Amsterdam.

Contents

  • Preface
  • Contributors
  • 1 Introduction
    Rens Bod, Remko Scha and Khalil Sima'an
  • Part I: The Basic Data‐Oriented Parsing
    • 2 A DOP Model for Phrase‐Structure Trees
      Rens Bod and Remko Scha
    • 3 Reconsidering the Probability Model for DOP
      Remko Bonnema and Remko Scha
    • 4 Encoding Frequency Information in Stochastic Parsing Models
      John Carroll and David Weir

  • Part II: Computational Issues
    • 5 Computational Complexity of Disamguation under DOP1
      Khalil Sima'an
    • 6 Parsing DOP with Monte-Carlo Techniques
      Jean‐Cédric Chappelier and Martin Rajman
    • 7 An Alternative Approach to Monte Carlo Parsing
      Remko Bonnema
    • 8 Efficient Parsing of DOP with PCFG‐Reductions
      Joshua Goodman
    • 9 An Approximation of DOP through Memory‐Based Learning
      Guy De Pauw
    • 10 Compositional Partial Parsing by Memory‐Based Sequence Learning
      Ido Dagan and Yuval Krymolowski

  • Part III: Richer Models
    • 11 Tree‐gram Parsing
      Khalil Sima'an
    • 12 A DOP Model for Lexical‐Functional Grammar
      Rens Bod and Ronald Kaplan
    • 13 A Data‐Driven Approach to Head‐Driven Phrase Structure
      Günter Neumann
    • 14 Tree Adjoining Grammars and Their Application to Statistical Parsing
      Aravind Joshi and Anoop Sarkar
    • 15 Localizing Dependencies and Supertagging
      Srinivas Bangalore
    • 16 Statistical Parsing with an Automatically Extracted Tree Adjoining Grammar
      David Chang
    • 17 Extending DOP Insertion

  • Part IV: Beyond Parsing
    • 18 Machine Translation with Tree‐DOP
      Arjen Poutsma
    • 19 Machine Translation Using LFG‐DOP
      Andy Way
    • 20 Aligning‐Based Learning versus Data‐Oriented Parsing
      Menno van Zaanen

  • Index

2/15/2003

ISBN (Paperback): 1575864363 (9781575864365)
ISBN (Cloth): 1575864355 (9781575864358)
ISBN (Electronic): 1575869330 (9781575869339)

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