Inductive Dependency Parsing

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Springer Science & Business Media, Aug 5, 2006 - Computers - 212 pages
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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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Contents

Introduction
1
12 The Need for Robust Disambiguation
5
13 Outline of the Book
6
Natural Language Parsing
9
21 Syntactic Representations
10
22 Two Notions of Parsing
12
221 Grammar Parsing
13
222 Text Parsing
16
416 Oracle Parsing
96
42 Features and Models
100
421 Feature Functions
101
422 Static Features
105
423 Dynamic Features
107
424 Feature Models
108
43 MemoryBased Learning
110
432 Learning Algorithm Parameters
112

223 Competence and Performance
19
23 Methods for Text Parsing
20
232 DataDriven Text Parsing
27
233 Converging Approaches
37
234 Inductive Dependency Parsing
40
24 Evaluation Criteria
41
243 Accuracy
42
244 Efficiency
43
Dependency Parsing
44
31 Dependency Grammar
46
311 The Notion of Dependency
47
312 Varieties of Dependency Grammar
50
32 Parsing with Dependency Representations
55
321 GrammarDriven Dependency Parsing
56
322 DataDriven Dependency Parsing
61
323 The Case for Dependency Parsing
66
33 A Framework for Dependency Parsing
67
331 Texts Sentences and Tokens
68
332 Dependency Graphs
69
333 Dependency Parsing
72
342 Transitions
74
343 Deterministic Parsing
76
344 Algorithm Analysis
79
345 Evaluation Criteria Revisited
85
Inductive Dependency Parsing
87
41 A Framework for Inductive Dependency Parsing
88
412 Inductive Inference
89
413 HistoryBased Models
90
414 Parsing Methods
92
415 Learning Methods
94
433 MemoryBased Language Processing
115
44 MaltParser
117
441 Architecture
118
442 Implementation
120
Treebank Parsing
121
51 Treebanks and Parsing
122
511 Treebank Evaluation
123
512 Treebank Learning
128
513 Treebanks for Dependency Parsing
129
52 Experimental Methodology
132
522 Models and Algorithms
139
523 Evaluation
140
53 Feature Model Parameters
142
531 PartofSpeech Context
143
532 Dependency Structure
145
533 Lexicalization
147
534 Efficiency
149
535 Learning Curves
155
54 Learning Algorithm Parameters
158
541 Neighbor Space and Distance Metric
159
542 Weighting Schemes
161
55 Final Evaluation
163
552 Related Work
168
553 Error Analysis
171
Conclusion
175
62 Future Directions
179
References
183
Index
209
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