Data-Oriented Parsing

by Bod, Rens

• ISBN13: 9781575864365
• ISBN10: 1575864363
• Format: Paperback
• Copyright: 2003-02-01
• Publisher: Stanford Univ Center for the Study

Summary

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.

Table of Contents

Preface	vii
Contributors	ix
1 Introduction	1	(10)
RENS BOD, REMKO SCHA AND KHALIL SIMA'AN
PART I: The Basic Data-Oriented Parsing Model	11	(50)
2 A DOP Model for Phrase-Structure Trees	13	(12)
RENS BOD AND REMKO SCHA
3 Reconsidering the Probability Model for DOP	25	(18)
REMKO BONNEMA AND REMKO SCHA
4 Encoding Frequency Information in Stochastic Parsing Models	43	(18)
JOHN CARROLL AND DAVID WEIR
PART II: Computational Issues	61	(128)
5 Computational Complexity of Disambiguation under DOP1	63	(20)
KHALIL SIMA'AN
6 Parsing DOP with Monte-Carlo Techniques	83	(24)
JEAN-CÉDRIC CHAPPELIER AND MARTIN RAJMAN
7 An Alternative Approach to Monte Carlo Parsing	107	(18)
REMKO BONNEMA
8 Efficient Parsing of DOP with PCFG-Reductions	125	(22)
JOSHUA GOODMAN
9 An Approximation of DOP through Memory-Based Learning	147	(22)
GUY DE PAUW
10 Compositional Partial Parsing by Memory-Based Sequence Learning	169	(20)
IDO DAGAN AND YUVAL KRYMOLOWSKI
PART III: Richer Models	189	(148)
11 Tree-gram Parsing	191	(20)
KHALIL SIMA'AN
12 A DOP Model for Lexical-Functional Grammar	211	(22)
RENS BOD AND RONALD KAPLAN
13 A Data-Driven Approach to Head-Driven Phrase Structure Grammar	233	(20)
GÜNTER NEUMANN
14 Tree Adjoining Grammars and Their Application to Statistical Parsing	253	(30)
ARAVIND JOSHI AND ANOOP SARKAR
15 Localizing Dependencies and Supertagging	283	(16)
SRINIVAS BANGALORE
16 Statistical Parsing with an Automatically Extracted Tree Adjoining Grammar	299	(18)
DAVID CHIANG
17 Extending DOP with Insertion	317	(20)
LARS HOOGWEG
PART IV: Beyond Parsing	337	(68)
18 Machine Translation with Tree-DOP	339	(20)
ARJEN POUTSMA
19 Machine Translation Using LFG-DOP	359	(26)
ANDY WAY
20 Alignment-Based Learning versus Data-Oriented Parsing	385	(20)
MENNO VAN ZAANEN
Index	405