The computational nature of language learning and evolution:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge, Mass.
MIT Press
2006
|
| Schriftenreihe: | Current studies in linguistics
43 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Includes bibliographical references and index |
| Beschreibung: | XVIII, 482 S. Ill. |
| ISBN: | 0262140942 |
Internformat
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| 100 | 1 | |a Niyogi, Partha |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a The computational nature of language learning and evolution |c Partha Niyogi |
| 264 | 1 | |a Cambridge, Mass. |b MIT Press |c 2006 | |
| 300 | |a XVIII, 482 S. |b Ill. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 1 | |a Current studies in linguistics |v 43 | |
| 500 | |a Includes bibliographical references and index | ||
| 650 | 7 | |a Changement linguistique |2 ram | |
| 650 | 7 | |a Langage - Acquisition |2 ram | |
| 650 | 7 | |a Langage et culture |2 ram | |
| 650 | 7 | |a Langage et langues |2 ram | |
| 650 | 7 | |a Linguistique - Informatique |2 ram | |
| 650 | 7 | |a Multilinguisme |2 ram | |
| 650 | 4 | |a Sprache | |
| 650 | 4 | |a Language acquisition | |
| 650 | 4 | |a Language and languages |x Origin | |
| 650 | 4 | |a Computational linguistics | |
| 650 | 4 | |a Linguistic change | |
| 650 | 4 | |a Multilingualism | |
| 650 | 4 | |a Language and culture | |
| 650 | 0 | 7 | |a Spracherwerb |0 (DE-588)4056458-7 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Computerlinguistik |0 (DE-588)4035843-4 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Evolution |0 (DE-588)4071050-6 |2 gnd |9 rswk-swf |
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Datensatz im Suchindex
| _version_ | 1804135608743886848 |
|---|---|
| adam_text | Contents
Preface xiii
Acknowledgments xvii
I The Problem 1
1 Introduction 3
1.1 Language Acquisition 8
1.2 Variation — Synchronic and Diachronic 13
1.3 More Examples of Change 16
1.3.1 Phonetic and Phonological Change 16
1.3.2 Syntactic Change 22
1.4 Perspective and Conceptual Issues 25
1.4.1 The Role of Learning 27
1.4.2 Populations versus Idiolects 28
1.4.3 Gradualness versus Abruptness (or the S Shaped Curve) 29
1.4.4 Different Time Scales of Evolution 30
1.4.5 Cautionary Aspects 31
1.5 Evolution in Linguistics and Biology 32
1.5.1 Scientific History 34
1.6 Summary of Results 37
1.6.1 Main Insights 39
1.7 Audience and Connections to Other Fields 42
1.7.1 Structure of the Book 44
II Language Learning 47
2 Language Acquisition: The Problem of Inductive Inference 49
CONTENTS vi
2.1 A Framework for Learning 50
2.1.1 Remarks 51
2.2 The Inductive Inference Approach 56
2.2.1 Discussion 60
2.2.2 Additional Results 63
2.3 The Probably Approximately Correct Model and the VC The¬
orem 71
2.3.1 Sets and Indicator Functions 71
2.3.2 Graded Distance 72
2.3.3 Examples and Learnability 72
2.3.4 The Vapnik Chervonenkis (VC) Theorem 74
2.3.5 Proof of Lower Bound for Learning 76
2.3.6 Implications 79
2.3.7 Complexity of Learning 81
2.3.8 Final Words 82
3 Language Acquisition: A Linguistic Treatment 83
3.1 Language Learning and the Poverty
of Stimulus 86
3.2 Constrained Grammars — Principles
and Parameters 88
3.2.1 Example: A Three Parameter System from Syntax . . 89
3.2.2 Example: Parameterized Metrical Stress in Phonology 94
3.3 Learning in the Principles and Parameters
Framework 96
3.4 Formal Analysis of the Triggering Learning Algorithm .... 100
3.4.1 Background 100
3.4.2 The Markov Formulation 102
3.4.3 Derivation of the Transition Probabilities for the Markov
TLA Structure 109
3.5 Conclusions Ill
3.6 Appendix 113
3.6.1 Unembedded Sentences For Parametric Grammars . . 113
3.6.2 Proof of Learnability Theorem 113
4 Language Acquisition: Memoryless Learning 117
4.1 Characterizing Convergence Times for the
Markov Chain Model 117
4.1.1 Some Transition Matrices and Their Convergence
Curves 118
vii CONTENTS
4.1.2 Absorption Times 123
4.1.3 Eigenvalue Rates of Convergence 123
4.2 Exploring Other Points 129
4.2.1 Changing the Algorithm 130
4.2.2 Distributional Assumptions 132
4.2.3 Natural Distributions CHILDES CORPUS 134
4.3 Batch Learning Upper and Lower Bounds: An Aside 135
4.4 Generalizations and Variations 138
4.4.1 Markov Chains and Learning Algorithms 138
4.4.2 Memoryless Learners 140
4.4.3 The Power of Memoryless Learners 141
4.5 Other Kinds of Learning Algorithms 142
4.6 Conclusions 144
4.7 Appendix: Proofs for Memoryless Algorithms 146
III Language Change 153
5 Language Change: A Preliminary Model 155
5.1 An Acquisition Based Model of Language
Change 157
5.2 A Preliminary Model 160
5.2.1 Learning by Individuals 161
5.2.2 Population Dynamics 162
5.2.3 Some Examples 164
5.3 Implications and Further Directions 177
5.3.1 An Example from Yiddish 177
5.3.2 Discussion 179
5.3.3 Future Directions 182
6 Language Change: Multiple Languages 187
6.1 Multiple Languages 187
6.1.1 The Language Acquisition Framework 187
6.1.2 From Language Learning to Population Dynamics . . 188
6.2 Example 1: A Three Parameter System 195
6.2.1 Homogeneous Initial Populations 196
6.2.2 Modeling Diachronic Trajectories 205
6.2.3 Nonhomogeneous Populations: Phase Space Plots . . 211
6.3 Example 2: Syntactic Change in French 218
6.3.1 The Parametric Subspace and Data 219
CONTENTS viii
6.3.2 The Case of Diachronic Syntactic Change in French . 220
6.3.3 Some Dynamical System Simulations 223
6.4 Conclusions 229
7 An Application to Portuguese 233
7.1 Portuguese: A Case Study 234
7.1.1 The Facts of Portuguese Language Change 234
7.2 The Logical Basis of Language Change 238
7.2.1 Galves Batch Learning Algorithm 239
7.2.2 Batch Subset Algorithm 246
7.2.3 Online Learning Algorithm (TLA) 247
7.3 Conclusions 248
8 An Application to Chinese Phonology 251
8.1 Phonological Merger in the Wenzhou Province 252
8.2 Two Forms in a Population 257
8.2.1 Case 1 257
8.2.2 Analysis 258
8.2.3 Case 2 260
8.2.4 Case 3 262
8.2.5 Case 4 263
8.2.6 Remarks and Discussion 264
8.3 Examining the Wenzhou Data Further 265
8.4 Error Driven Models 268
8.4.1 Asymmetric Errors 269
8.4.2 Bifurcations and the Actuation Problem 270
8.5 Discussion 271
8.5.1 Sound Change 271
8.5.2 Connections to Population Biology 272
8.6 Conclusions 273
9 A Model of Cultural Evolution and Its Application to Lan¬
guage 275
9.1 Background 275
9.2 The Cavalli Sforza and Feldman Theory 277
9.3 Instantiating the CF Model for Languages 279
9.3.1 One Parameter Models 279
9.3.2 An Alternative Approach 281
9.3.3 Transforming NB Models into the CF Framework . . . 282
ix CONTENTS
9.4 CF Models for Some Simple Learning
Algorithms 284
9.4.1 TLA and Its Evolution 284
9.4.2 Batch and Cue Based Learners 288
9.4.3 A Historical Example 289
9.5 A Generalized NB Model for Neighborhood Effects 298
9.5.1 A Specific Choice of Neighborhood Mapping 300
9.6 A Note on Oblique Transmission 302
9.7 Conclusions 303
10 Variations and Case Studies 305
10.1 Finite Populations 305
10.1.1 Finite Populations 306
10.1.2 Stochastic Dynamics 306
10.1.3 Evolutionary Behavior as a Function of iV 307
10.2 Spatial Effects 314
10.2.1 Spatial Variation and Dialect Formation 314
10.2.2 A General Spatial Model 316
10.3 Multilingual Learners 320
10.3.1 Bilingualism Modeled as a Lambda Factor 322
10.3.2 Further Remarks 327
10.3.3 A Bilingual Model for French 330
10.4 Conclusions 336
IV The Origin of Language 339
11 The Origin of Communicative Systems: Communicative Ef¬
ficiency 341
11.1 Communicative Efficiency of Language 343
11.1.1 Comnmnicability in Animal. Human, and Machine
Communication 345
11.2 Connnunicability for Linguistic Systems 346
11.2.1 Basic Notions 346
11.2.2 Probability of Events and a Communicability Function 349
11.3 Reaching the Highest Communicability 351
11.3.1 A Special Case of Finite Languages 351
11.3.2 Generalizations 359
11.4 Implications for Learning 359
11.4.1 Estimating P 360
CONTENTS x
11.4.2 Estimating Q 362
11.4.3 Sample Complexity Bounds 363
11.5 Communicative Efficiency and Linguistic
Structure 366
11.5.1 Phonemic Contrasts and Lexical Structure 367
11.5.2 Functional Load and Communicative Efficiency .... 368
11.5.3 Perceptual Confusibility and Functional Load 371
12 The Origin of Communicative Systems: Linguistic Coher¬
ence and Communicative Fitness 375
12.1 General Model 376
12.1.1 The Class of Languages 376
12.1.2 Fitness, Reproduction, and Learning 377
12.1.3 Population Dynamics 378
12.2 Dynamics of a Fully Symmetric System 379
12.2.1 Fixed Points 380
12.2.2 Stability of the Fixed Points 385
12.2.3 The Bifurcation Scenario 391
12.3 Fidelity of Learning Algorithms 392
12.3.1 Memoryless Learning • ¦ •¦ 393
12.3.2 Batch Learning 395
12.4 Asymmetric A Matrices 398
12.4.1 Breaking the Symmetry of the A Matrix 398
12.4.2 Random Off Diagonal Elements 399
12.4.3 Final Remarks 402
12.5 Conclusions 402
13 The Origin of Communicative Systems: Linguistic Coher¬
ence and Social Learning 405
13.1 Learning Only from Parents 406
13.2 Social Learning: Learning from Everybody 408
13.2.1 The Symmetric Assumption 408
13.2.2 Coherence for n = 2 409
13.3 Coherence for General n 415
13.3.1 Cue Frequency Based Batch Learner 415
13.3.2 Evolutionary Dynamics of Batch Learner 416
13.4 Proofs of Evolutionary Dynamics Results 418
13.4.1 Preliminaries 418
13.4.2 Equilibria 420
13.4.3 Stability 422
xi CONTENTS
13.4.4 Bifurcations 426
13.5 Coherence for a Memory less Learner 432
13.6 Learning in Connected Societies 433
13.6.1 Language Evolution in Locally Connected Societies . . 434
13.6.2 Magnetic Systems: The Ising Model 435
13.6.3 Analogies and Implications 438
13.7 Conclusions 441
V Conclusions 443
14 Conclusions 445
14.1 A Summary of the Major Insights 446
14.1.1 Learning and Evolution 446
14.1.2 Bifurcations in the History of Language 448
14.1.3 Natural Selection and the Emergence of Language . . 449
14.2 Future Directions 449
14.2.1 Empirical Validation 453
14.2.2 Connections to Other Disciplines 456
14.3 A Concluding Thought 458
Bibliography 459
Index 479
|
| adam_txt |
Contents
Preface xiii
Acknowledgments xvii
I The Problem 1
1 Introduction 3
1.1 Language Acquisition 8
1.2 Variation — Synchronic and Diachronic 13
1.3 More Examples of Change 16
1.3.1 Phonetic and Phonological Change 16
1.3.2 Syntactic Change 22
1.4 Perspective and Conceptual Issues 25
1.4.1 The Role of Learning 27
1.4.2 Populations versus Idiolects 28
1.4.3 Gradualness versus Abruptness (or the S Shaped Curve) 29
1.4.4 Different Time Scales of Evolution 30
1.4.5 Cautionary Aspects 31
1.5 Evolution in Linguistics and Biology 32
1.5.1 Scientific History 34
1.6 Summary of Results 37
1.6.1 Main Insights 39
1.7 Audience and Connections to Other Fields 42
1.7.1 Structure of the Book 44
II Language Learning 47
2 Language Acquisition: The Problem of Inductive Inference 49
CONTENTS ' vi
2.1 A Framework for Learning 50
2.1.1 Remarks 51
2.2 The Inductive Inference Approach 56
2.2.1 Discussion 60
2.2.2 Additional Results 63
2.3 The Probably Approximately Correct Model and the VC The¬
orem 71
2.3.1 Sets and Indicator Functions 71
2.3.2 Graded Distance 72
2.3.3 Examples and Learnability 72
2.3.4 The Vapnik Chervonenkis (VC) Theorem 74
2.3.5 Proof of Lower Bound for Learning 76
2.3.6 Implications 79
2.3.7 Complexity of Learning 81
2.3.8 Final Words 82
3 Language Acquisition: A Linguistic Treatment 83
3.1 Language Learning and the Poverty
of Stimulus 86
3.2 Constrained Grammars — Principles
and Parameters 88
3.2.1 Example: A Three Parameter System from Syntax . . 89
3.2.2 Example: Parameterized Metrical Stress in Phonology 94
3.3 Learning in the Principles and Parameters
Framework 96
3.4 Formal Analysis of the Triggering Learning Algorithm . 100
3.4.1 Background 100
3.4.2 The Markov Formulation 102
3.4.3 Derivation of the Transition Probabilities for the Markov
TLA Structure 109
3.5 Conclusions Ill
3.6 Appendix 113
3.6.1 Unembedded Sentences For Parametric Grammars . . 113
3.6.2 Proof of Learnability Theorem 113
4 Language Acquisition: Memoryless Learning 117
4.1 Characterizing Convergence Times for the
Markov Chain Model 117
4.1.1 Some Transition Matrices and Their Convergence
Curves 118
vii CONTENTS
4.1.2 Absorption Times 123
4.1.3 Eigenvalue Rates of Convergence 123
4.2 Exploring Other Points 129
4.2.1 Changing the Algorithm 130
4.2.2 Distributional Assumptions 132
4.2.3 Natural Distributions CHILDES CORPUS 134
4.3 Batch Learning Upper and Lower Bounds: An Aside 135
4.4 Generalizations and Variations 138
4.4.1 Markov Chains and Learning Algorithms 138
4.4.2 Memoryless Learners 140
4.4.3 The Power of Memoryless Learners 141
4.5 Other Kinds of Learning Algorithms 142
4.6 Conclusions 144
4.7 Appendix: Proofs for Memoryless Algorithms 146
III Language Change 153
5 Language Change: A Preliminary Model 155
5.1 An Acquisition Based Model of Language
Change 157
5.2 A Preliminary Model 160
5.2.1 Learning by Individuals 161
5.2.2 Population Dynamics 162
5.2.3 Some Examples 164
5.3 Implications and Further Directions 177
5.3.1 An Example from Yiddish 177
5.3.2 Discussion 179
5.3.3 Future Directions 182
6 Language Change: Multiple Languages 187
6.1 Multiple Languages 187
6.1.1 The Language Acquisition Framework 187
6.1.2 From Language Learning to Population Dynamics . . 188
6.2 Example 1: A Three Parameter System 195
6.2.1 Homogeneous Initial Populations 196
6.2.2 Modeling Diachronic Trajectories 205
6.2.3 Nonhomogeneous Populations: Phase Space Plots . . 211
6.3 Example 2: Syntactic Change in French 218
6.3.1 The Parametric Subspace and Data 219
CONTENTS viii
6.3.2 The Case of Diachronic Syntactic Change in French . 220
6.3.3 Some Dynamical System Simulations 223
6.4 Conclusions 229
7 An Application to Portuguese 233
7.1 Portuguese: A Case Study 234
7.1.1 The Facts of Portuguese Language Change 234
7.2 The Logical Basis of Language Change 238
7.2.1 Galves Batch Learning Algorithm 239
7.2.2 Batch Subset Algorithm 246
7.2.3 Online Learning Algorithm (TLA) 247
7.3 Conclusions 248
8 An Application to Chinese Phonology 251
8.1 Phonological Merger in the Wenzhou Province 252
8.2 Two Forms in a Population 257
8.2.1 Case 1 257
8.2.2 Analysis 258
8.2.3 Case 2 260
8.2.4 Case 3 262
8.2.5 Case 4 263
8.2.6 Remarks and Discussion 264
8.3 Examining the Wenzhou Data Further 265
8.4 Error Driven Models 268
8.4.1 Asymmetric Errors 269
8.4.2 Bifurcations and the Actuation Problem 270
8.5 Discussion 271
8.5.1 Sound Change 271
8.5.2 Connections to Population Biology 272
8.6 Conclusions 273
9 A Model of Cultural Evolution and Its Application to Lan¬
guage 275
9.1 Background 275
9.2 The Cavalli Sforza and Feldman Theory 277
9.3 Instantiating the CF Model for Languages 279
9.3.1 One Parameter Models 279
9.3.2 An Alternative Approach 281
9.3.3 Transforming NB Models into the CF Framework . . . 282
ix CONTENTS
9.4 CF Models for Some Simple Learning
Algorithms 284
9.4.1 TLA and Its Evolution 284
9.4.2 Batch and Cue Based Learners 288
9.4.3 A Historical Example 289
9.5 A Generalized NB Model for Neighborhood Effects 298
9.5.1 A Specific Choice of Neighborhood Mapping 300
9.6 A Note on Oblique Transmission 302
9.7 Conclusions 303
10 Variations and Case Studies 305
10.1 Finite Populations 305
10.1.1 Finite Populations 306
10.1.2 Stochastic Dynamics 306
10.1.3 Evolutionary Behavior as a Function of iV 307
10.2 Spatial Effects 314
10.2.1 Spatial Variation and Dialect Formation 314
10.2.2 A General Spatial Model 316
10.3 Multilingual Learners 320
10.3.1 Bilingualism Modeled as a Lambda Factor 322
10.3.2 Further Remarks 327
10.3.3 A Bilingual Model for French 330
10.4 Conclusions 336
IV The Origin of Language 339
11 The Origin of Communicative Systems: Communicative Ef¬
ficiency 341
11.1 Communicative Efficiency of Language 343
11.1.1 Comnmnicability in Animal. Human, and Machine
Communication 345
11.2 Connnunicability for Linguistic Systems 346
11.2.1 Basic Notions 346
11.2.2 Probability of Events and a Communicability Function 349
11.3 Reaching the Highest Communicability 351
11.3.1 A Special Case of Finite Languages 351
11.3.2 Generalizations 359
11.4 Implications for Learning 359
11.4.1 Estimating P 360
CONTENTS x
11.4.2 Estimating Q 362
11.4.3 Sample Complexity Bounds 363
11.5 Communicative Efficiency and Linguistic
Structure 366
11.5.1 Phonemic Contrasts and Lexical Structure 367
11.5.2 Functional Load and Communicative Efficiency . 368
11.5.3 Perceptual Confusibility and Functional Load 371
12 The Origin of Communicative Systems: Linguistic Coher¬
ence and Communicative Fitness 375
12.1 General Model 376
12.1.1 The Class of Languages 376
12.1.2 Fitness, Reproduction, and Learning 377
12.1.3 Population Dynamics 378
12.2 Dynamics of a Fully Symmetric System 379
12.2.1 Fixed Points 380
12.2.2 Stability of the Fixed Points 385
12.2.3 The Bifurcation Scenario 391
12.3 Fidelity of Learning Algorithms 392
12.3.1 Memoryless Learning • ¦ •¦ 393
12.3.2 Batch Learning 395
12.4 Asymmetric A Matrices 398
12.4.1 Breaking the Symmetry of the A Matrix 398
12.4.2 Random Off Diagonal Elements 399
12.4.3 Final Remarks 402
12.5 Conclusions 402
13 The Origin of Communicative Systems: Linguistic Coher¬
ence and Social Learning 405
13.1 Learning Only from Parents 406
13.2 Social Learning: Learning from Everybody 408
13.2.1 The Symmetric Assumption 408
13.2.2 Coherence for n = 2 409
13.3 Coherence for General n 415
13.3.1 Cue Frequency Based Batch Learner 415
13.3.2 Evolutionary Dynamics of Batch Learner 416
13.4 Proofs of Evolutionary Dynamics Results 418
13.4.1 Preliminaries 418
13.4.2 Equilibria 420
13.4.3 Stability 422
xi CONTENTS
13.4.4 Bifurcations 426
13.5 Coherence for a Memory less Learner 432
13.6 Learning in Connected Societies 433
13.6.1 Language Evolution in Locally Connected Societies . . 434
13.6.2 Magnetic Systems: The Ising Model 435
13.6.3 Analogies and Implications 438
13.7 Conclusions 441
V Conclusions 443
14 Conclusions 445
14.1 A Summary of the Major Insights 446
14.1.1 Learning and Evolution 446
14.1.2 Bifurcations in the History of Language 448
14.1.3 Natural Selection and the Emergence of Language . . 449
14.2 Future Directions 449
14.2.1 Empirical Validation 453
14.2.2 Connections to Other Disciplines 456
14.3 A Concluding Thought 458
Bibliography 459
Index 479 |
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| id | DE-604.BV021751378 |
| illustrated | Illustrated |
| index_date | 2024-07-02T15:32:17Z |
| indexdate | 2024-07-09T20:43:13Z |
| institution | BVB |
| isbn | 0262140942 |
| language | English |
| lccn | 2005058015 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-014964593 |
| oclc_num | 494703893 |
| open_access_boolean | |
| owner | DE-12 DE-83 DE-29 DE-19 DE-BY-UBM |
| owner_facet | DE-12 DE-83 DE-29 DE-19 DE-BY-UBM |
| physical | XVIII, 482 S. Ill. |
| publishDate | 2006 |
| publishDateSearch | 2006 |
| publishDateSort | 2006 |
| publisher | MIT Press |
| record_format | marc |
| series | Current studies in linguistics |
| series2 | Current studies in linguistics |
| spelling | Niyogi, Partha Verfasser aut The computational nature of language learning and evolution Partha Niyogi Cambridge, Mass. MIT Press 2006 XVIII, 482 S. Ill. txt rdacontent n rdamedia nc rdacarrier Current studies in linguistics 43 Includes bibliographical references and index Changement linguistique ram Langage - Acquisition ram Langage et culture ram Langage et langues ram Linguistique - Informatique ram Multilinguisme ram Sprache Language acquisition Language and languages Origin Computational linguistics Linguistic change Multilingualism Language and culture Spracherwerb (DE-588)4056458-7 gnd rswk-swf Computerlinguistik (DE-588)4035843-4 gnd rswk-swf Evolution (DE-588)4071050-6 gnd rswk-swf Spracherwerb (DE-588)4056458-7 s Computerlinguistik (DE-588)4035843-4 s Evolution (DE-588)4071050-6 s DE-604 Current studies in linguistics 43 (DE-604)BV000009279 43 HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014964593&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Niyogi, Partha The computational nature of language learning and evolution Current studies in linguistics Changement linguistique ram Langage - Acquisition ram Langage et culture ram Langage et langues ram Linguistique - Informatique ram Multilinguisme ram Sprache Language acquisition Language and languages Origin Computational linguistics Linguistic change Multilingualism Language and culture Spracherwerb (DE-588)4056458-7 gnd Computerlinguistik (DE-588)4035843-4 gnd Evolution (DE-588)4071050-6 gnd |
| subject_GND | (DE-588)4056458-7 (DE-588)4035843-4 (DE-588)4071050-6 |
| title | The computational nature of language learning and evolution |
| title_auth | The computational nature of language learning and evolution |
| title_exact_search | The computational nature of language learning and evolution |
| title_exact_search_txtP | The computational nature of language learning and evolution |
| title_full | The computational nature of language learning and evolution Partha Niyogi |
| title_fullStr | The computational nature of language learning and evolution Partha Niyogi |
| title_full_unstemmed | The computational nature of language learning and evolution Partha Niyogi |
| title_short | The computational nature of language learning and evolution |
| title_sort | the computational nature of language learning and evolution |
| topic | Changement linguistique ram Langage - Acquisition ram Langage et culture ram Langage et langues ram Linguistique - Informatique ram Multilinguisme ram Sprache Language acquisition Language and languages Origin Computational linguistics Linguistic change Multilingualism Language and culture Spracherwerb (DE-588)4056458-7 gnd Computerlinguistik (DE-588)4035843-4 gnd Evolution (DE-588)4071050-6 gnd |
| topic_facet | Changement linguistique Langage - Acquisition Langage et culture Langage et langues Linguistique - Informatique Multilinguisme Sprache Language acquisition Language and languages Origin Computational linguistics Linguistic change Multilingualism Language and culture Spracherwerb Computerlinguistik Evolution |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014964593&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV000009279 |
| work_keys_str_mv | AT niyogipartha thecomputationalnatureoflanguagelearningandevolution |