DATA-ORIENTED PARSING (STUDIES IN COMPUTATIONAL Bod, Scha, Sima'an Like New

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Item specifics

Condition
Like New
A book in excellent condition. Cover is shiny and undamaged, and the dust jacket is included for hard covers. No missing or damaged pages, no creases or tears, and no underlining/highlighting of text or writing in the margins. May be very minimal identifying marks on the inside cover. Very minimal wear and tear. See all condition definitionsopens in a new window or tab
Seller Notes
“No markings, damage, or signs of wear.”
Book Title
Data-Oriented Parsing (Studies in Computational Linguistics)
ISBN-10
1575864363
Educational Level
Adult & Further Education
Level
Advanced
Genre
LANGUAGE ARTS & DISCIPLINES
ISBN
9781575864365
Category

About this product

Product Identifiers

Publisher
Csli Publications/Center for T.H.E. Study of Language & Information
ISBN-10
1575864363
ISBN-13
9781575864365
eBay Product ID (ePID)
2446502

Product Key Features

Number of Pages
448 Pages
Publication Name
Data-Oriented Parsing
Language
English
Subject
Intelligence (Ai) & Semantics, Natural Language Processing, Linguistics / General
Publication Year
2003
Type
Textbook
Author
Remko Scha
Subject Area
Computers, Language Arts & Disciplines
Series
Studies in Computational Linguistics Ser.
Format
Trade Paperback

Dimensions

Item Height
0.9 in
Item Weight
19.7 Oz
Item Length
8.9 in
Item Width
6.1 in

Additional Product Features

Intended Audience
Scholarly & Professional
LCCN
2003-000639
Dewey Edition
21
Illustrated
Yes
Dewey Decimal
410/.285
Table Of Content
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
Synopsis
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.
LC Classification Number
P98.5.P38D38 2003

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