Skip to main content
Shopping cart: items Cart

Distributed for Center for the Study of Language and Information

Data-Oriented Parsing

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.

448 pages | 6 x 9 | © 2003

Studies in Computational Linguistics

Language and Linguistics: General Language and Linguistics


Table of Contents

Preface
Contributors
1. Introduction
Rens Bod, Remko Scha and Khalil Sima-an
PART I: The Basic Data-Oriented Parsing Model
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 Disambiguation under DOP1
Khalil Sima’an
6. Parsing DOP with Monte-Carlo Techniques
Jean-Cedric 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 Grammar
Gunter 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 Chiang
17. Extending DOP with Insertion
Lars Hoogweg
PART IV: Beyond Parsing
18. Machine Translation with Tree-DOP
Arjen Poutsma
19. Machine Translation Using LFG-DOP
Andy Way
20. Alignment-Based Learning versus Data-Oriented Parsing
Menno van Zaanen
Index

Be the first to know

Get the latest updates on new releases, special offers, and media highlights when you subscribe to our email lists!

Sign up here for updates about the Press