Semisupervised learning in computational linguistics:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Boca Raton, FL [u.a.]
Chapman & Hall/CRC
2008
|
| Schriftenreihe: | Computer science and data analysis series
|
| Schlagworte: | |
| Online-Zugang: | Table of contents only Inhaltsverzeichnis |
| Beschreibung: | Literaturverz. S. 277 - 299 |
| Beschreibung: | XI, 308 S. graph. Darst. |
| ISBN: | 9781584885597 |
Internformat
MARC
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| 010 | |a 2007022858 | ||
| 020 | |a 9781584885597 |9 978-1-58488-559-7 | ||
| 035 | |a (OCoLC)436204478 | ||
| 035 | |a (DE-599)DNB 2007022858 | ||
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| 084 | |a ST 306 |0 (DE-625)143654: |2 rvk | ||
| 100 | 1 | |a Abney, Steven Paul |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Semisupervised learning in computational linguistics |c Steven Abney |
| 264 | 1 | |a Boca Raton, FL [u.a.] |b Chapman & Hall/CRC |c 2008 | |
| 300 | |a XI, 308 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Computer science and data analysis series | |
| 500 | |a Literaturverz. S. 277 - 299 | ||
| 650 | 7 | |a Linguistique - Informatique |2 ram | |
| 650 | 4 | |a Linguistique informatique - Étude et enseignement (Supérieur) | |
| 650 | 4 | |a Computational linguistics |x Study and teaching (Higher) | |
| 650 | 0 | 7 | |a Teilüberwachtes Lernen |0 (DE-588)4782452-9 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Computerlinguistik |0 (DE-588)4035843-4 |2 gnd |9 rswk-swf |
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| 689 | 0 | 1 | |a Teilüberwachtes Lernen |0 (DE-588)4782452-9 |D s |
| 689 | 0 | |5 DE-604 | |
| 856 | 4 | |u http://www.loc.gov/catdir/toc/ecip0719/2007022858.html |3 Table of contents only | |
| 856 | 4 | 2 | |m Digitalisierung UB Regensburg |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016077260&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-016077260 | ||
Datensatz im Suchindex
| _version_ | 1804137131656871936 |
|---|---|
| adam_text | Contents
1
Introduction
1
1.1
A brief history
.......................... 1
1.1.1
Probabilistic methods in computational linguistics
. . 1
1.1.2
Supervised and unsupervised training
......... 2
1.1.3
Semisupervised learning
................. 3
1.2
Semisupervised learning
..................... 4
1.2.1
Major varieties of learning problem
........... 4
1.2.2
Motivation
........................ 6
1.2.3
Evaluation
......................... 7
. 1.2.4
Active learning
...................... 8
1.3
Organization and assumptions
.................. 8
1.3.1
Leading ideas
....................... 8
1.3.2
Mathematical background
................ 10
1.3.3
Notation
.......................... 11
2
Self-training and Co-training
13
2.1
Classification
........................... 13
2.1.1
The standard setting
................... 13
2.1.2
Features and rules
.................... 14
2.1.3
Decision lists
....................... 16
2.2
Self-training
............................ 18
2.2.1
The algorithm
....................... 19
2.2.2
Parameters and variants
................. 20
2.2.3
Evaluation
......................... 23
2.2.4
Symmetry of features and instances
.......... 25
2.2.5
Related algorithms
.................... 27
2.3
Co-Training
............................ 28
3
Applications of Self-Training and Co-Training
31
3.1
Part-of-speech tagging
...................... 31
3.2
Information extraction
...................... 33
3.3
Parsing
.............................. 35
3.4
Word senses
............................ 36
3.4.1
WordNet
......................... 36
3.4.2
Word-sense disambiguation
............... 38
3.4.3
Taxonomie
inference
................... 40
vii
VIH
4
Classification
43
4.1
Two simple classifiers
....................... 43
4.1.1
Naive
Bayes
........................ 43
4.1.2
fc-nearest-neighbor classifier
............... 45
4.2
Abstract setting
.......................... 48
4.2.1
Function approximation
................. 48
4.2.2
Defining success
...................... 50
4.2.3
Fit and simplicity
..................... 52
4.3
Evaluating detectors and classifiers that abstain
........ 53
4.3.1
Confidence-rated classifiers
............... 53
4.3.2
Measures for detection
.................. 54
4.3.3
Idealized performance curves
.............. 57
4.3.4
The multiclass case
.................... 59
4.4
Binary classifiers and ECOC
................... 62
5
Mathematics for Boundary-Oriented Methods
67
5.1
Linear separators
......................... 67
5.1.1
Representing
a
hyperplane................ 67
5.1.2
Eliminating the threshold
................ 69
5.1.3
The point-normal form
.................. 70
5.1.4
Naive
Bayes
decision boundary
............. 72
5.2
The gradient
........................... 74
5.2.1
Graphs and domains
................... 74
5.2.2
Convexity
......................... 76
5.2.3
Differentiation of vector and matrix expressions
.... 79
5.2.4
An example: linear regression
.............. 81
5.3
Constrained optimization
.................... 83
5.3.1
Optimization
....................... 83
5.3.2
Equality constraints
................... 84
5.3.3
Inequality constraints
.................. 87
5.3.4
The Wolfe dual
...................... 91
6
Boundary-Oriented Methods
95
6.1
The perceptron
.......................... 97
6.1.1
The algorithm
....................... 97
6.1.2
An example
........................ 99
6.1.3
Convergence
........................ 100
6.1.4
The perceptron algorithm as gradient descent
..... 101
6.2
Game self-teaching
........................ 103
6.3
Boosting
.............................. 105
6.3.1
Abstention
........................ 110
6.3.2
Semisupervised boosting
.................
Ill
6.3.3
Co-boosting
........................ 113
6.4
Support Vector Machines (SVMs)
................ 114
6.4.1
The margin
........................ 114
6.4.2
Maximizing the margin
................. 116
6.4.3
The nonseparable case
.................. 119
6.4.4
Slack in the separable case
................ 121
6.4.5
Multiple slack points
................... 123
6.4.6
Transductive SVMs
.................... 125
6.4.7
Training a transductive SVM
.............. 127
6.5
Null-category noise model
.................... 129
7
Clustering
131
7.1
Cluster and label
.........................131
7.2
Clustering concepts
........................132
7.2.1
Objective
.........................132
7.2.2
Distance and similarity
..................133
7.2.3
Graphs
...........................136
7.3
Hierarchical clustering
......................137
7.4
Self-training revisited
.......................139
7.4.1
fc-means clustering
....................139
7.4.2
Pseudo
relevance feedback
................140
7.5
Graph mincut
...........................143
7.6
Label propagation
........................146
7.6.1
Clustering by propagation
................146
7.6.2
Self-training as propagation
...............147
7.6.3
Co-training as propagation
...............150
7.7
Bibliographic notes
........................152
8
Generative Models
153
8.1
Gaussian mixtures
........................ 153
8.1.1
Definition and geometric interpretation
........ 153
8.1.2
The linear discriminant decision boundary
....... 156
8.1.3
Decision-directed approximation
............ 159
8.1.4
McLachlan s algorithm
.................. 162
8.2
The EM algorithm
........................ 163
8.2.1
Maximizing likelihood
.................. 163
8.2.2
Relative frequency estimation
.............. 164
8.2.3
Divergence
........................ 166
8.2.4
The EM algorithm
.................... 169
9
Agreement Constraints
175
9.1
Co-training
............................175
9.1.1
The conditional independence assumption
.......176
9.1.2
The power of conditionalindependence
.........178
9.2
Agreement-based self-teaching
..................182
9.3
Random fields
...........................184
9.3.1
Applied to self-training and co-training
.........184
9.3.2
Gibbs sampling
......................186
9.3.3
Markov chains and random walks
............187
9.4
Bibliographic notes
........................192
10
Propagation Methods
193
10.1
Label propagation
........................194
10.2
Random walks
..........................196
10.3
Harmonic functions
........................198
10.4
Fluids
...............................203
10.4.1
Flow
............................203
10.4.2
Pressure
..........................205
10.4.3
Conservation of energy
..................209
10.4.4
Thomson s principle
...................210
10.5
Computing the solution
.....................213
10.6
Graph mincuts revisited
.....................215
10.7
Bibliographic notes
........................220
11
Mathematics for Spectral Methods
221
11.1
Some basic concepts
.......................221
11.1.1
The norm of a vector
...................221
11.1.2
Matrices as linear operators
...............222
11.1.3
The column space
....................222
11.2
Eigenvalues and eigenvectors
..................224
11.2.1
Definition of eigenvalues and eigenvectors
.......224
11.2.2
Diagonalization
......................225
11.2.3
Orthogonal diagonalization
...............226
11.3
Eigenvalues and the scaling effects of a matrix
.........227
11.3.1
Matrix norms
.......................227
11.3.2
The Rayleigh quotient
..................228
11.3.3
The
2x2
case
......................230
11.3.4
The general case
.....................232
11.3.5
The
Courant-Fischer minimax
theorem
.........234
11.4
Bibliographic notes
........................236
12
Spectral Methods
237
12.1
Simple harmonic motion
.....................237
12.1.1
Harmonics
.........................237
12.1.2
Mixtures of harmonics
..................239
12.1.3
An oscillating particle
..................241
12.1.4
A vibrating string
....................243
12.2
Spectra of matrices and graphs
.................251
12.2.1
The spectrum of a matrix
................252
12.2.2
Relating matrices and graphs
..............253
12.2.3
The Laplacian matrix and graph spectrum
.......256
12.3
Spectral clustering
........................257
12.3.1
The second smallest eigenvector of the Laplacian
. . . 257
Xl
12.3.2
The cut size and the Laplacian
............. 259
12.3.3
Approximating cut size
................. 260
12.3.4
Minimizing cut size
.................... 262
12.3.5
Ratiocut
.......................... 263
12.4
Spectral methods for semisupervised learning
.........265
12.4.1
Harmonics and harmonic functions
...........265
12.4.2
Eigenvalues and energy
.................267
12.4.3
The Laplacian and random fields
............268
12.4.4
Harmonic functions and the Laplacian
.........270
12.4.5
Using the Laplacian for regularization
.........272
12.4.6
Transduction to induction
................274
12.5
Bibliographic notes
........................275
Bibliography
277
Index
301
|
| adam_txt |
Contents
1
Introduction
1
1.1
A brief history
. 1
1.1.1
Probabilistic methods in computational linguistics
. . 1
1.1.2
Supervised and unsupervised training
. 2
1.1.3
Semisupervised learning
. 3
1.2
Semisupervised learning
. 4
1.2.1
Major varieties of learning problem
. 4
1.2.2
Motivation
. 6
1.2.3
Evaluation
. 7
. 1.2.4
Active learning
. 8
1.3
Organization and assumptions
. 8
1.3.1
Leading ideas
. 8
1.3.2
Mathematical background
. 10
1.3.3
Notation
. 11
2
Self-training and Co-training
13
2.1
Classification
. 13
2.1.1
The standard setting
. 13
2.1.2
Features and rules
. 14
2.1.3
Decision lists
. 16
2.2
Self-training
. 18
2.2.1
The algorithm
. 19
2.2.2
Parameters and variants
. 20
2.2.3
Evaluation
. 23
2.2.4
Symmetry of features and instances
. 25
2.2.5
Related algorithms
. 27
2.3
Co-Training
. 28
3
Applications of Self-Training and Co-Training
31
3.1
Part-of-speech tagging
. 31
3.2
Information extraction
. 33
3.3
Parsing
. 35
3.4
Word senses
. 36
3.4.1
WordNet
. 36
3.4.2
Word-sense disambiguation
. 38
3.4.3
Taxonomie
inference
. 40
vii
VIH
4
Classification
43
4.1
Two simple classifiers
. 43
4.1.1
Naive
Bayes
. 43
4.1.2
fc-nearest-neighbor classifier
. 45
4.2
Abstract setting
. 48
4.2.1
Function approximation
. 48
4.2.2
Defining success
. 50
4.2.3
Fit and simplicity
. 52
4.3
Evaluating detectors and classifiers that abstain
. 53
4.3.1
Confidence-rated classifiers
. 53
4.3.2
Measures for detection
. 54
4.3.3
Idealized performance curves
. 57
4.3.4
The multiclass case
. 59
4.4
Binary classifiers and ECOC
. 62
5
Mathematics for Boundary-Oriented Methods
67
5.1
Linear separators
. 67
5.1.1
Representing
a
hyperplane. 67
5.1.2
Eliminating the threshold
. 69
5.1.3
The point-normal form
. 70
5.1.4
Naive
Bayes
decision boundary
. 72
5.2
The gradient
. 74
5.2.1
Graphs and domains
. 74
5.2.2
Convexity
. 76
5.2.3
Differentiation of vector and matrix expressions
. 79
5.2.4
An example: linear regression
. 81
5.3
Constrained optimization
. 83
5.3.1
Optimization
. 83
5.3.2
Equality constraints
. 84
5.3.3
Inequality constraints
. 87
5.3.4
The Wolfe dual
. 91
6
Boundary-Oriented Methods
95
6.1
The perceptron
. 97
6.1.1
The algorithm
. 97
6.1.2
An example
. 99
6.1.3
Convergence
. 100
6.1.4
The perceptron algorithm as gradient descent
. 101
6.2
Game self-teaching
. 103
6.3
Boosting
. 105
6.3.1
Abstention
. 110
6.3.2
Semisupervised boosting
.
Ill
6.3.3
Co-boosting
. 113
6.4
Support Vector Machines (SVMs)
. 114
6.4.1
The margin
. 114
6.4.2
Maximizing the margin
. 116
6.4.3
The nonseparable case
. 119
6.4.4
Slack in the separable case
. 121
6.4.5
Multiple slack points
. 123
6.4.6
Transductive SVMs
. 125
6.4.7
Training a transductive SVM
. 127
6.5
Null-category noise model
. 129
7
Clustering
131
7.1
Cluster and label
.131
7.2
Clustering concepts
.132
7.2.1
Objective
.132
7.2.2
Distance and similarity
.133
7.2.3
Graphs
.136
7.3
Hierarchical clustering
.137
7.4
Self-training revisited
.139
7.4.1
fc-means clustering
.139
7.4.2
Pseudo
relevance feedback
.140
7.5
Graph mincut
.143
7.6
Label propagation
.146
7.6.1
Clustering by propagation
.146
7.6.2
Self-training as propagation
.147
7.6.3
Co-training as propagation
.150
7.7
Bibliographic notes
.152
8
Generative Models
153
8.1
Gaussian mixtures
. 153
8.1.1
Definition and geometric interpretation
. 153
8.1.2
The linear discriminant decision boundary
. 156
8.1.3
Decision-directed approximation
. 159
8.1.4
McLachlan's algorithm
. 162
8.2
The EM algorithm
. 163
8.2.1
Maximizing likelihood
. 163
8.2.2
Relative frequency estimation
. 164
8.2.3
Divergence
. 166
8.2.4
The EM algorithm
. 169
9
Agreement Constraints
175
9.1
Co-training
.175
9.1.1
The conditional independence assumption
.176
9.1.2
The power of conditionalindependence
.178
9.2
Agreement-based self-teaching
.182
9.3
Random fields
.184
9.3.1
Applied to self-training and co-training
.184
9.3.2
Gibbs sampling
.186
9.3.3
Markov chains and random walks
.187
9.4
Bibliographic notes
.192
10
Propagation Methods
193
10.1
Label propagation
.194
10.2
Random walks
.196
10.3
Harmonic functions
.198
10.4
Fluids
.203
10.4.1
Flow
.203
10.4.2
Pressure
.205
10.4.3
Conservation of energy
.209
10.4.4
Thomson's principle
.210
10.5
Computing the solution
.213
10.6
Graph mincuts revisited
.215
10.7
Bibliographic notes
.220
11
Mathematics for Spectral Methods
221
11.1
Some basic concepts
.221
11.1.1
The norm of a vector
.221
11.1.2
Matrices as linear operators
.222
11.1.3
The column space
.222
11.2
Eigenvalues and eigenvectors
.224
11.2.1
Definition of eigenvalues and eigenvectors
.224
11.2.2
Diagonalization
.225
11.2.3
Orthogonal diagonalization
.226
11.3
Eigenvalues and the scaling effects of a matrix
.227
11.3.1
Matrix norms
.227
11.3.2
The Rayleigh quotient
.228
11.3.3
The
2x2
case
.230
11.3.4
The general case
.232
11.3.5
The
Courant-Fischer minimax
theorem
.234
11.4
Bibliographic notes
.236
12
Spectral Methods
237
12.1
Simple harmonic motion
.237
12.1.1
Harmonics
.237
12.1.2
Mixtures of harmonics
.239
12.1.3
An oscillating particle
.241
12.1.4
A vibrating string
.243
12.2
Spectra of matrices and graphs
.251
12.2.1
The spectrum of a matrix
.252
12.2.2
Relating matrices and graphs
.253
12.2.3
The Laplacian matrix and graph spectrum
.256
12.3
Spectral clustering
.257
12.3.1
The second smallest eigenvector of the Laplacian
. . . 257
Xl
12.3.2
The cut size and the Laplacian
. 259
12.3.3
Approximating cut size
. 260
12.3.4
Minimizing cut size
. 262
12.3.5
Ratiocut
. 263
12.4
Spectral methods for semisupervised learning
.265
12.4.1
Harmonics and harmonic functions
.265
12.4.2
Eigenvalues and energy
.267
12.4.3
The Laplacian and random fields
.268
12.4.4
Harmonic functions and the Laplacian
.270
12.4.5
Using the Laplacian for regularization
.272
12.4.6
Transduction to induction
.274
12.5
Bibliographic notes
.275
Bibliography
277
Index
301 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Abney, Steven Paul |
| author_facet | Abney, Steven Paul |
| author_role | aut |
| author_sort | Abney, Steven Paul |
| author_variant | s p a sp spa |
| building | Verbundindex |
| bvnumber | BV022872178 |
| callnumber-first | P - Language and Literature |
| callnumber-label | P98 |
| callnumber-raw | P98.3 |
| callnumber-search | P98.3 |
| callnumber-sort | P 298.3 |
| callnumber-subject | P - Philology and Linguistics |
| classification_rvk | ES 900 ST 306 |
| ctrlnum | (OCoLC)436204478 (DE-599)DNB 2007022858 |
| dewey-full | 410.285 |
| dewey-hundreds | 400 - Language |
| dewey-ones | 410 - Linguistics |
| dewey-raw | 410.285 |
| dewey-search | 410.285 |
| dewey-sort | 3410.285 |
| dewey-tens | 410 - Linguistics |
| discipline | Sprachwissenschaft Informatik Literaturwissenschaft |
| discipline_str_mv | Sprachwissenschaft Informatik Literaturwissenschaft |
| format | Book |
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| id | DE-604.BV022872178 |
| illustrated | Illustrated |
| index_date | 2024-07-02T18:47:39Z |
| indexdate | 2024-07-09T21:07:25Z |
| institution | BVB |
| isbn | 9781584885597 |
| language | English |
| lccn | 2007022858 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016077260 |
| oclc_num | 436204478 |
| open_access_boolean | |
| owner | DE-19 DE-BY-UBM DE-355 DE-BY-UBR DE-29 |
| owner_facet | DE-19 DE-BY-UBM DE-355 DE-BY-UBR DE-29 |
| physical | XI, 308 S. graph. Darst. |
| publishDate | 2008 |
| publishDateSearch | 2008 |
| publishDateSort | 2008 |
| publisher | Chapman & Hall/CRC |
| record_format | marc |
| series2 | Computer science and data analysis series |
| spelling | Abney, Steven Paul Verfasser aut Semisupervised learning in computational linguistics Steven Abney Boca Raton, FL [u.a.] Chapman & Hall/CRC 2008 XI, 308 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Computer science and data analysis series Literaturverz. S. 277 - 299 Linguistique - Informatique ram Linguistique informatique - Étude et enseignement (Supérieur) Computational linguistics Study and teaching (Higher) Teilüberwachtes Lernen (DE-588)4782452-9 gnd rswk-swf Computerlinguistik (DE-588)4035843-4 gnd rswk-swf Computerlinguistik (DE-588)4035843-4 s Teilüberwachtes Lernen (DE-588)4782452-9 s DE-604 http://www.loc.gov/catdir/toc/ecip0719/2007022858.html Table of contents only Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016077260&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Abney, Steven Paul Semisupervised learning in computational linguistics Linguistique - Informatique ram Linguistique informatique - Étude et enseignement (Supérieur) Computational linguistics Study and teaching (Higher) Teilüberwachtes Lernen (DE-588)4782452-9 gnd Computerlinguistik (DE-588)4035843-4 gnd |
| subject_GND | (DE-588)4782452-9 (DE-588)4035843-4 |
| title | Semisupervised learning in computational linguistics |
| title_auth | Semisupervised learning in computational linguistics |
| title_exact_search | Semisupervised learning in computational linguistics |
| title_exact_search_txtP | Semisupervised learning in computational linguistics |
| title_full | Semisupervised learning in computational linguistics Steven Abney |
| title_fullStr | Semisupervised learning in computational linguistics Steven Abney |
| title_full_unstemmed | Semisupervised learning in computational linguistics Steven Abney |
| title_short | Semisupervised learning in computational linguistics |
| title_sort | semisupervised learning in computational linguistics |
| topic | Linguistique - Informatique ram Linguistique informatique - Étude et enseignement (Supérieur) Computational linguistics Study and teaching (Higher) Teilüberwachtes Lernen (DE-588)4782452-9 gnd Computerlinguistik (DE-588)4035843-4 gnd |
| topic_facet | Linguistique - Informatique Linguistique informatique - Étude et enseignement (Supérieur) Computational linguistics Study and teaching (Higher) Teilüberwachtes Lernen Computerlinguistik |
| url | http://www.loc.gov/catdir/toc/ecip0719/2007022858.html http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016077260&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT abneystevenpaul semisupervisedlearningincomputationallinguistics |