Inductive Dependency Parsing

By: Joakim Nivre

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Hardcover | 28 June 2006

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232 Pages

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23.5 x 15.88 x 1.27

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This book describes the framework of inductive dependency parsing, a methodology for robust and efficient syntactic analysis of unrestricted natural language text. Coverage includes a theoretical analysis of central models and algorithms, and an empirical evaluation of memory-based dependency parsing using data from Swedish and English. A one-stop reference to dependency-based parsing of natural language, it will interest researchers and system developers in language technology, and is suitable for graduate or advanced undergraduate courses.

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"The book demonstrates Nivre's impressive ability to explain dependency grammar and dependency parsing clearly and succinctly to a wide audience. ... The logical progression and the clarity with which this is done is one of the many strengths of this book. ... Get Nivre's book; read it; and enjoy it! The excellent and thorough reference list alone is worth it, constituting a good ten percent of the book." (Christer Samuelsson, Computational Linguistics, Vol. 33 (2), 2007)

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You Can Find This Book In

Non-Fiction Computing & I.T.Computer Science Artificial Intelligence Natural Language & Machine Translation Language & Linguistics Linguistics Computational Linguistics Grammar, Syntax & Morphology Business Applications

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Paperback

Published: 18th November 2010

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Introduction	p. 1
Inductive Dependency Parsing	p. 1
The Need for Robust Disambiguation	p. 5
Outline of the Book	p. 6
Natural Language Parsing	p. 9
Syntactic Representations	p. 10
Two Notions of Parsing	p. 12
Grammar Parsing	p. 13
Text Parsing	p. 16
Competence and Performance	p. 19
Methods for Text Parsing	p. 20
Grammar-Driven Text Parsing	p. 20
Data-Driven Text Parsing	p. 27
Converging Approaches	p. 37
Inductive Dependency Parsing	p. 40
Evaluation Criteria	p. 41
Robustness	p. 41
Disambiguation	p. 41
Accuracy	p. 42
Efficiency	p. 43
Dependency Parsing	p. 45
Dependency Grammar	p. 46
The Notion of Dependency	p. 47
Varieties of Dependency Grammar	p. 50
Parsing with Dependency Representations	p. 55
Grammar-Driven Dependency Parsing	p. 56
Data-Driven Dependency Parsing	p. 61
The Case for Dependency Parsing	p. 66
A Framework for Dependency Parsing	p. 67
Texts, Sentences and Tokens	p. 68
Dependency Graphs	p. 69
Dependency Parsing	p. 72
Parsing Algorithm	p. 72
Configurations	p. 72
Transitions	p. 74
Deterministic Parsing	p. 76
Algorithm Analysis	p. 79
Evaluation Criteria Revisited	p. 85
Inductive Dependency Parsing	p. 87
A Framework for Inductive Dependency Parsing	p. 88
Data-Driven Text Parsing	p. 88
Inductive Inference	p. 89
History-Based Models	p. 90
Parsing Methods	p. 92
Learning Methods	p. 94
Oracle Parsing	p. 96
Features and Models	p. 100
Feature Functions	p. 101
Static Features	p. 105
Dynamic Features	p. 107
Feature Models	p. 108
Memory-Based Learning	p. 110
Memory-Based Learning and Classification	p. 110
Learning Algorithm Parameters	p. 112
Memory-Based Language Processing	p. 115
MaltParser	p. 117
Architecture	p. 118
Implementation	p. 120
Treebank Parsing	p. 121
Treebanks and Parsing	p. 122
Treebank Evaluation	p. 123
Treebank Learning	p. 128
Treebanks for Dependency Parsing	p. 129
Experimental Methodology	p. 132
Treebank Data	p. 132
Models and Algorithms	p. 139
Evaluation	p. 140
Feature Model Parameters	p. 142
Part-of-Speech Context	p. 143
Dependency Structure	p. 145
Lexicalization	p. 147
Efficiency	p. 149
Learning Curves	p. 155
Learning Algorithm Parameters	p. 158
Neighbor Space and Distance Metric	p. 159
Weighting Schemes	p. 161
Final Evaluation	p. 163
Accuracy and Efficiency	p. 163
Related Work	p. 168
Error Analysis	p. 171
Conclusion	p. 175
Main Contributions	p. 175
Future Directions	p. 179
References	p. 183
Index	p. 209
Table of Contents provided by Ingram. All Rights Reserved.

Inductive Dependency Parsing

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Hardcover

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