Foundations of statistical natural language processing:
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge, Mass. [u.a.]
MIT Press
2001
|
| Ausgabe: | 4. printing |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Klappentext |
| Beschreibung: | XXXVII, 680 S. graph. Darst. |
| ISBN: | 0262133601 9780262133609 |
Internformat
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| 100 | 1 | |a Manning, Christopher D. |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Foundations of statistical natural language processing |c Christopher D. Manning ; Hinrich Schütze |
| 250 | |a 4. printing | ||
| 264 | 1 | |a Cambridge, Mass. [u.a.] |b MIT Press |c 2001 | |
| 300 | |a XXXVII, 680 S. |b graph. Darst. | ||
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Datensatz im Suchindex
| _version_ | 1823781135690235904 |
|---|---|
| adam_text |
Contents
List of Tables
xv
List of Figures
xxi
Table of Notations
xxv
Preface
xxix
Road Map
xxxv
I Preliminaries
1
1
Introduction
3
1.1
Rationalist and Empiricist Approaches to Language
4
1.2
Scientific Content
7
1.2.1
Questions that linguistics should answer
8
1.2.2
Non-categorical phenomena in language
11
1.2.3
Language and cognition as probabilistic
phenomena
15
1.3
The Ambiguity of Language: Why NLP Is Difficult
17
1.4
Dirty Hands
19
1.4.1
Lexical resources
19
1.4.2
Word counts
20
1.4.3
Zipf'slaws
23
1.4.4
Collocations
29
1.4.5
Concordances
31
1.5
Further Reading
34
1.6
Exercises
35
2
Mathematical Foundations
39
2.1
Elementary Probability Theory
40
2.1.1
Probability spaces
40
2.1.2
Conditional probability and independence
2.1.3
Bayes'
theorem
43
2.1.4
Random variables
45
2.1.5
Expectation and variance
46
2.1.6
Notation
47
2.1.7
Joint and conditional distributions
48
2.1.8
Determining
Ρ
48
2.1.9
Standard distributions
50
2.1.10
Bayesian statistics
54
2.1.11
Exercises
59
42
2.2
Essential Information Theory
60
2.2.1
Entropy
61
2.2.2
Joint entropy and conditional entropy
63
2.2.3
Mutual information
66
2.2.4
The noisy channel model
68
2.2.5
Relative entropy or Kullback-Leibler divergence
2.2.6
The relation to language: Cross entropy
73
2.2.7
The entropy of English
76
2.2.8
Perplexity
78
2.2.9
Exercises
78
2.3
Further Reading
79
Linguistic Essentials
81
3.1
Parts of Speech and Morphology
81
3.1.1
Nouns and pronouns
83
3.1.2
Words that accompany nouns: Determiners and
adjectives
87
3.1.3
Verbs
88
3.1.4
Other parts of speech
91
3.2
Phrase Structure
93
3.2.1
Phrase structure grammars
96
3.2.2
Dependency: Arguments and adjuncts
101
3.2.3
X' theory
106
3.2.4
Phrase structure ambiguity
107
72
3.3
Semantics and Pragmatics
109
3.4
Other Areas
112
3.5
Further Reading
113
3.6
Exercises
114
Corpus-Based Work
117
4.1
Getting Set Up
118
4.1.1
Computers
118
4.1.2
Corpora
118
4.1.3
Software
120
4.2
Looking at Text
123
4.2.1
Low-level formatting issues
123
4.2.2
Tokenization: What is a word?
124
4.2.3
Morphology
131
4.2.4
Sentences
134
4.3
Marked-up Data
136
4.3.1
Markup schemes
137
4.3.2
Grammatical tagging
139
4.4
Further Reading
145
4.5
Exercises
147
II Words
149
Collocations
5.1
Frequency
151
153
5.2
Mean and Variance
157
5.3
Hypothesis Testing
162
5.3.1
The
f
test
163
5.3.2
Hypothesis testing of differences
166
5.3.3
Pearson's chi-square test
169
5.3.4
Likelihood ratios
172
5.4
Mutual Information
178
5.5
The Notion of Collocation
183
5.6
Further Reading
187
Statistical Inference:
η
-gram Models over Sparse Data
6.1
Bins: Forming Equivalence Classes
192
6.1.1
Reliability vs. discrimination
192
6.1.2
η
-gram
models
192
191
6.1.3
Building
n-gram
models
195
6.2
Statistical Estimators
196
6.2.1
Maximum Likelihood Estimation (MLE)
197
6.2.2
Laplace's law, Lidstone's law and the
Jeffreys-Perks law
202
6.2.3
Held out estimation
205
6.2.4
Cross-validation (deleted estimation)
210
6.2.5
Good-Turing estimation
212
6.2.6
Briefly noted
216
6.3
Combining Estimators
217
6.3.1
Simple linear interpolation
218
6.3.2
Katz's backing-off
219
6.3.3
General linear interpolation
220
6.3.4
Briefly noted
222
6.3.5
Language models for Austen
223
6.4
Conclusions
224
6.5
Further Reading
225
6.6
Exercises
225
7
Word Sense Disambiguation
229
7.1
Methodological Preliminaries
232
7.1.1
Supervised and unsupervised learning
232
7.1.2
Pseudowords
233
7.1.3
Upper and lower bounds on performance
233
7.2
Supervised Disambiguation
235
7.2.1
Bayesian classification
235
7.2.2
An information-theoretic approach
239
7.3
Dictionary-Based Disambiguation
241
7.3.1
Disambiguation based on sense definitions
242
7.3.2
Thesaurus-based disambiguation
244
7.3.3
Disambiguation based on translations in a
second-language corpus
247
7.3.4
One sense per discourse, one sense per
collocation
249
7.4
Unsupervised Disambiguation
252
7.5
What Is a Word Sense?
256
7.6
Further Reading
260
7.7
Exercises
262
8
Lexical Acquisition
265
8.1
Evaluation Measures
267
8.2
Verb Subcategorization
271
8.3
Attachment Ambiguity
278
8.3.1
Hindle and Rooth
(1993) 280
8.3.2
General remarks on PP attachment
284
8.4
Selecţionai
Preferences
288
8.5
Semantic Similarity
294
8.5.1
Vector space measures
296
8.5.2
Probabilistic measures
303
8.6
The Role of Lexical Acquisition in Statistical NLP
8.7
Further Reading
312
308
III Grammar
315
9
Markov Models
317
9.1
Markov Models
318
9.2
Hidden Markov Models
320
9.2.1
Why use HMMs?
322
9.2.2
General form of an
HMM
324
9.3
The Three Fundamental Questions for HMMs
325
9.3.1
Finding the probability of an observation
326
9.3.2
Finding the best state sequence
331
9.3.3
The third problem: Parameter estimation
333
9.4
HMMs: Implementation, Properties, and Variants
336
9.4.1
Implementation
336
9.4.2
Variants
337
9.4.3
Multiple input observations
338
9.4.4
Initialization of parameter values
339
9.5
Further Reading
339
10
Part-of-Speech Tagging
341
10.1
The Information Sources in Tagging
10.2
Markov Model Taggers
345
10.2.1
The probabilistic model
345
10.2.2
The Viterbi algorithm
349
10.2.3
Variations
351
10.3
Hidden Markov Model Taggers
356
343
10.3.1
Applying HMMs to
POS
tagging
357
10.3.2
The effect of initialization on
HMM
training
10.4
Transformation-Based Learning of Tags
361
10.4.1
Transformations
362
10.4.2
The learning algorithm
364
10.4.3
Relation to other models
365
10.4.4
Automata
367
10.4.5
Summary
369
10.5
Other Methods, Other Languages
370
10.5.1
Other approaches to tagging
370
10.5.2
Languages other than English
371
10.6
Tagging Accuracy and Uses of Taggers
371
10.6.1
Tagging accuracy
371
10.6.2
Applications of tagging
374
10.7
Further Reading
377
10.8
Exercises
379
359
11
Probabilistic Context Free Grammars
381
11.1
Some Features of PCFGs
386
11.2
Questions for PCFGs
388
11.3
The Probability of a String
392
11.3.1
Using inside probabilities
392
11.3.2
Using outside probabilities
394
11.3.3
Finding the most likely parse for a sentence
396
11.3.4
Training a PCFG
398
11.4
Problems with the Inside-Outside Algorithm
401
11.5
Further Reading
402
11.6
Exercises
404
12
Probabilistic Parsing
407
12.1
Some Concepts
408
12.1.1
Parsing for disambiguation
408
12.1.2
Treebanks
412
12.1.3
Parsing models vs
.
language models
414
12.1.4
Weakening the independence assumptions of
PCFGs
416
12.1.5
Tree probabilities and derivational probabilities
421
12.1.6
There's more than one way to do it
423
12.1.7
Phrase structure grammars and dependency
grammars
428
12.1.8
Evaluation
431
12.1.9
Equivalent models
437
12.1.10
Building parsers: Search methods
439
12.1.11
Use of the geometric mean
442
12.2
Some Approaches
443
12.2.1
Non-lexicalized
treebank
grammars
443
12.2.2
Lexicalized models using derivational histories
12.2.3
Dependency-based models
451
12.2.4
Discussion
454
12.3
Further Reading
456
12.4
Exercises
458
448
IV Applications and Techniques
461
13
Statistical Alignment and Machine Translation
463
13.1
Text Alignment
466
13.1.1
Aligning sentences and paragraphs
467
13.1.2
Length-based methods
471
13.1.3
Offset alignment by signal processing
techniques
475
13.1.4
Lexical methods of sentence alignment
478
13.1.5
Summary
484
13.1.6
Exercises
484
13.2
Word Alignment
484
13.3
Statistical Machine Translation
486
13.4
Further Reading
492
14
Clustering
495
14.1
Hierarchical Clustering
500
14.1.1
Single-link and complete-link clustering
503
14.1.2
Group-average agglomerative clustering
507
14.1.3
An application: Improving a language model
509
14.1.4
Top-down clustering
512
14.2
Non-Hierarchical Clustering
514
14.2.1
K-means
515
14.2.2
The EM algorithm
518
14.3
Further Reading
527
14.4
Exercises
528
15
Topics
in Information Retrieval
529
15.1
Some Background on Information Retrieval
530
15.1.1
Common design features of
IR
systems
532
15.1.2
Evaluation measures
534
15.1.3
The probability ranking principle (PRP)
538
15.2
The Vector Space Model
539
15.2.1
Vector similarity
540
15.2.2
Term weighting
541
15.3
Term Distribution Models
544
15.3.1
The
Poisson
distribution
545
15.3.2
The two-Poisson model
548
15.3.3
The
К
mixture
549
15.3.4
Inverse document frequency
551
15.3.5
Residual inverse document frequency
553
15.3.6
Usage of term distribution models
554
15.4
Latent Semantic Indexing
554
15.4.1
Least-squares methods
557
15.4.2
Singular Value Decomposition
558
15.4.3
Latent Semantic Indexing in
IR
564
15.5
Discourse Segmentation
566
15.5.1
TextTiling
567
15.6
Further Reading
570
15.7
Exercises
573
16
Text Categorization
575
16.1
Decision Trees
578
16.2
Maximum Entropy Modeling
589
16.2.1
Generalized iterative scaling
591
16.2.2
Application to text categorization
594
16.3
Perceptrons
597
16.4
к
Nearest Neighbor Classification
604
16.5
Further Reading
607
Tiny Statistical Tables
609
Bibliography
611
Index
657
Statistical approaches to processing natural language text have become dominant in recent years. This
foundational text is the first comprehensive introduction to statistical natural language processing (NLP)
to appear. The book contains all the theory and algorithms needed for building NLP tools. It provides broad
but rigorous coverage of mathematical and linguistic foundations, as well as detailed discussion of statis¬
tical methods, allowing students and researchers to construct their own implementations. The book covers
collocation finding, word sense disambiguation, probabilistic parsing, information retrieval, and other
applications. |
| any_adam_object | 1 |
| author | Manning, Christopher D. Schütze, Hinrich 1964- |
| author_GND | (DE-588)112281672 |
| author_facet | Manning, Christopher D. Schütze, Hinrich 1964- |
| author_role | aut aut |
| author_sort | Manning, Christopher D. |
| author_variant | c d m cd cdm h s hs |
| building | Verbundindex |
| bvnumber | BV013990164 |
| callnumber-first | P - Language and Literature |
| callnumber-label | P98 |
| callnumber-raw | P98.5.S83 |
| callnumber-search | P98.5.S83 |
| callnumber-sort | P 298.5 S83 |
| callnumber-subject | P - Philology and Linguistics |
| classification_rvk | ES 910 ST 306 |
| classification_tum | DAT 710f LIN 080f |
| ctrlnum | (OCoLC)248015662 (DE-599)BVBBV013990164 |
| dewey-full | 410/.285 |
| dewey-hundreds | 400 - Language |
| dewey-ones | 410 - Linguistics |
| dewey-raw | 410/.285 |
| dewey-search | 410/.285 |
| dewey-sort | 3410 3285 |
| dewey-tens | 410 - Linguistics |
| discipline | Sprachwissenschaft Informatik Literaturwissenschaft Sprachwissenschaften |
| edition | 4. printing |
| format | Book |
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| id | DE-604.BV013990164 |
| illustrated | Illustrated |
| indexdate | 2025-02-11T17:00:27Z |
| institution | BVB |
| isbn | 0262133601 9780262133609 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-009575598 |
| oclc_num | 248015662 |
| open_access_boolean | |
| owner | DE-384 DE-91G DE-BY-TUM DE-11 |
| owner_facet | DE-384 DE-91G DE-BY-TUM DE-11 |
| physical | XXXVII, 680 S. graph. Darst. |
| publishDate | 2001 |
| publishDateSearch | 2001 |
| publishDateSort | 2001 |
| publisher | MIT Press |
| record_format | marc |
| spelling | Manning, Christopher D. Verfasser aut Foundations of statistical natural language processing Christopher D. Manning ; Hinrich Schütze 4. printing Cambridge, Mass. [u.a.] MIT Press 2001 XXXVII, 680 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Computational linguistics Statistical methods Natürliche Sprache (DE-588)4041354-8 gnd rswk-swf Sprachverarbeitung (DE-588)4116579-2 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Computerlinguistik (DE-588)4035843-4 gnd rswk-swf Sprachstatistik (DE-588)4182534-2 gnd rswk-swf Natürliche Sprache (DE-588)4041354-8 s Sprachstatistik (DE-588)4182534-2 s Computerlinguistik (DE-588)4035843-4 s 1\p DE-604 Sprachverarbeitung (DE-588)4116579-2 s Statistik (DE-588)4056995-0 s 2\p DE-604 Schütze, Hinrich 1964- Verfasser (DE-588)112281672 aut Digitalisierung UB Augsburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009575598&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Augsburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009575598&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Manning, Christopher D. Schütze, Hinrich 1964- Foundations of statistical natural language processing Computational linguistics Statistical methods Natürliche Sprache (DE-588)4041354-8 gnd Sprachverarbeitung (DE-588)4116579-2 gnd Statistik (DE-588)4056995-0 gnd Computerlinguistik (DE-588)4035843-4 gnd Sprachstatistik (DE-588)4182534-2 gnd |
| subject_GND | (DE-588)4041354-8 (DE-588)4116579-2 (DE-588)4056995-0 (DE-588)4035843-4 (DE-588)4182534-2 |
| title | Foundations of statistical natural language processing |
| title_auth | Foundations of statistical natural language processing |
| title_exact_search | Foundations of statistical natural language processing |
| title_full | Foundations of statistical natural language processing Christopher D. Manning ; Hinrich Schütze |
| title_fullStr | Foundations of statistical natural language processing Christopher D. Manning ; Hinrich Schütze |
| title_full_unstemmed | Foundations of statistical natural language processing Christopher D. Manning ; Hinrich Schütze |
| title_short | Foundations of statistical natural language processing |
| title_sort | foundations of statistical natural language processing |
| topic | Computational linguistics Statistical methods Natürliche Sprache (DE-588)4041354-8 gnd Sprachverarbeitung (DE-588)4116579-2 gnd Statistik (DE-588)4056995-0 gnd Computerlinguistik (DE-588)4035843-4 gnd Sprachstatistik (DE-588)4182534-2 gnd |
| topic_facet | Computational linguistics Statistical methods Natürliche Sprache Sprachverarbeitung Statistik Computerlinguistik Sprachstatistik |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009575598&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009575598&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT manningchristopherd foundationsofstatisticalnaturallanguageprocessing AT schutzehinrich foundationsofstatisticalnaturallanguageprocessing |