Natural language processing: a machine learning perspective
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge ; New York ; Melbourne ; New Delhi ; Singapore
Cambridge University Press
2021
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Klappentext |
| Beschreibung: | xvi, 470 Seiten Diagramme |
| ISBN: | 9781108420211 1108420214 |
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Datensatz im Suchindex
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| adam_text | Contents Preface Acknowledgements Notation page xi xv xvi Part I Basics l 1 3 Introduction 1.1 What is NLP? 1.2 NLP Tasks 1.3 NLP from a Machine Learning Perspective Summary Chapter Notes Exercises 2 Counting Relative Frequencies 2.1 Probabilistic Modelling 2.2 и-gram Language Models 2.3 A Probabilistic Model for Text Classification Summary Chapter Notes Exercises 3 Feature Vectors 3.1 Modelling Documents in Vector Spaces 3.2 Multi-Class Classification 3.3 Discriminative Linear Models 3.4 Vector Spaces and Model Training Summary Chapter Notes Exercises 3 5 21 22 22 23 25 25 32 41 47 47 48 50 50 61 65 67 69 69 69 v
vi Contents 4 Discriminative Linear Classifiers 5 6 73 4.1 Log-Linear Models 4.2 SGD Training of SVMs 4.3 A Generalised Linear Model 4.4 Choosing and Combining Models Summary Chapter Notes Exercises 73 81 84 89 94 94 94 A Perspective from Information Theory 98 5.1 The Maximum Entropy Principle 5.2 KL-Divergence and Cross-Entropy 5.3 Mutual Information Summary Chapter Notes Exercises 98 105 109 116 116 116 Hidden Variables 120 6.1 Expectation Maximisation 6.2 Using EM for Training Models with Hidden Variables 6.3 Theory behind EM Summary Chapter Notes Exercises 120 127 139 142 142 142 Part II Structures 145 7 147 8 Generative Sequence Labelling 7.1 Sequence Labelling 7.2 Hidden Markov Models 7.3 Finding Marginal Probabilities 7.4 EM for Unsupervised HMM Training Summary Chapter Notes Exercises 147 148 154 159 166 166 167 Discriminative Sequence Labelling 169 8.1 8.2 8.3 8.4 Locally Trained Models for Discriminative Sequence Labelling The Label Bias Problem Conditional Random Fields Structured Perceptron 169 172 174 182
Contents 9 vii 8.5 Structured S VM Summary Chapter Notes Exercises 185 187 188 188 Sequence Segmentation 190 9.1 Segmentation by Sequence Labelling 9.2 Discriminative Models for Sequence Segmentation 9.3 Structured Perceptron and Beam Search Summary Chapter Notes Exercises 10 Predicting TreeStructures 10.1 Generative Constituent Parsing 10.2 More Features for Constituent Parsing 10.3 Reranking 10.4 Beyond Sequences and Trees Summary Chapter Notes Exercises 190 195 206 209 210 210 212 212 221 228 231 231 231 232 11 Transition-BasedMethods for Structured Prediction 235 11.1 Transition-Based Structured Prediction 11.2 Transition-Based Constituent Parsing 11.3 Transition-Based Dependency Parsing 11.4 Joint Parsing Models Summary Chapter Notes Exercises 235 242 246 251 256 256 257 12 Bayesian Network 12.1 A General Probabilistic Model 12.2 Training Bayesian Networks 12.3 Inference 12.4 Latent Dirichlet Allocation 12.5 Bayesian IBM Model 1 Summary Chapter Notes Exercises 259 259 262 272 275 283 284 284 284
viii Contents Part III Deep Learning 287 13 Neural Network 289 13.1 From One Layer to Multiple Layers 13.2 Building a Text Classifierwithout Manual Features 13.3 Improving Neural Network Training Summary Chapter Notes Exercises 14 Representation Learning 14.1 Recurrent Neural Network 14.2 Neural Attention 14.3 Representing Trees 14.4 Representing Graphs 14.5 Analysing Representation 14.6 More on Neural Network Training Summary Chapter Notes Exercises 15 Neural Structured Prediction 15.1 Local Graph-Based Models 15.2 Local Transition-Based Models 15.3 Global Structured Models Summary Chapter Notes Exercises 16 Working with Two Texts 16.1 Sequence-to-Sequence Models 16.2 Text Matching Models Summary Chapter Notes Exercises 17 Pre-training and Transfer Learning 17.1 17.2 17.3 Neural Language Models and Word Embedding Contextualised Word Representations Transfer Learning 289 300 306 310 310 311 314 314 320 325 329 333 334 338 338 339 343 343 351 358 367 367 367 370 370 382 392 392 393 396 396 409 414
Contents Summary Chapter Notes Exercises 18 Deep Latent Variable Models 18.1 Introducing Latent Variables into a Neural Network Model 18.2 Working with Categorical Latent Variables 18.3 Working with Structured Latent Variables 18.4 Variational Inference 18.5 Neural Topic Models 18.6 VAEs for Language Modelling Summary Chapter Notes Exercises Bibliography Index IX 419 420 420 423 423 424 429 434 443 444 450 450 450 453 468
An amazingly compact, and at the same time comprehensive, introduction and reference to natural language processing (NLP). It describes the NLP basics, then employs this knowledge to solve typical NLP problems. It achieves very high coverage of NLP through a clever abstraction to typical high-level tasks, such as sequence labelling. Finally, it explains the topics in deep learning. The book captivates through its simple elegance, depth, and accessibility to a wide range of readers from undergrads to experienced researchers. Iryna Gurevych, Technical University of Darmstadt, Germany An excellent introduction to the field of natural language processing including recent advances in deep learning. By organising the material in terms of machine learning techniques - instead of the more traditional division by linguistic levels or applications - the authors are able to discuss different topics within a single coherent framework, with a gradual progression from basic notions to more complex material. Joakim Nivre, Uppsala University 1
|
| adam_txt |
Contents Preface Acknowledgements Notation page xi xv xvi Part I Basics l 1 3 Introduction 1.1 What is NLP? 1.2 NLP Tasks 1.3 NLP from a Machine Learning Perspective Summary Chapter Notes Exercises 2 Counting Relative Frequencies 2.1 Probabilistic Modelling 2.2 и-gram Language Models 2.3 A Probabilistic Model for Text Classification Summary Chapter Notes Exercises 3 Feature Vectors 3.1 Modelling Documents in Vector Spaces 3.2 Multi-Class Classification 3.3 Discriminative Linear Models 3.4 Vector Spaces and Model Training Summary Chapter Notes Exercises 3 5 21 22 22 23 25 25 32 41 47 47 48 50 50 61 65 67 69 69 69 v
vi Contents 4 Discriminative Linear Classifiers 5 6 73 4.1 Log-Linear Models 4.2 SGD Training of SVMs 4.3 A Generalised Linear Model 4.4 Choosing and Combining Models Summary Chapter Notes Exercises 73 81 84 89 94 94 94 A Perspective from Information Theory 98 5.1 The Maximum Entropy Principle 5.2 KL-Divergence and Cross-Entropy 5.3 Mutual Information Summary Chapter Notes Exercises 98 105 109 116 116 116 Hidden Variables 120 6.1 Expectation Maximisation 6.2 Using EM for Training Models with Hidden Variables 6.3 Theory behind EM Summary Chapter Notes Exercises 120 127 139 142 142 142 Part II Structures 145 7 147 8 Generative Sequence Labelling 7.1 Sequence Labelling 7.2 Hidden Markov Models 7.3 Finding Marginal Probabilities 7.4 EM for Unsupervised HMM Training Summary Chapter Notes Exercises 147 148 154 159 166 166 167 Discriminative Sequence Labelling 169 8.1 8.2 8.3 8.4 Locally Trained Models for Discriminative Sequence Labelling The Label Bias Problem Conditional Random Fields Structured Perceptron 169 172 174 182
Contents 9 vii 8.5 Structured S VM Summary Chapter Notes Exercises 185 187 188 188 Sequence Segmentation 190 9.1 Segmentation by Sequence Labelling 9.2 Discriminative Models for Sequence Segmentation 9.3 Structured Perceptron and Beam Search Summary Chapter Notes Exercises 10 Predicting TreeStructures 10.1 Generative Constituent Parsing 10.2 More Features for Constituent Parsing 10.3 Reranking 10.4 Beyond Sequences and Trees Summary Chapter Notes Exercises 190 195 206 209 210 210 212 212 221 228 231 231 231 232 11 Transition-BasedMethods for Structured Prediction 235 11.1 Transition-Based Structured Prediction 11.2 Transition-Based Constituent Parsing 11.3 Transition-Based Dependency Parsing 11.4 Joint Parsing Models Summary Chapter Notes Exercises 235 242 246 251 256 256 257 12 Bayesian Network 12.1 A General Probabilistic Model 12.2 Training Bayesian Networks 12.3 Inference 12.4 Latent Dirichlet Allocation 12.5 Bayesian IBM Model 1 Summary Chapter Notes Exercises 259 259 262 272 275 283 284 284 284
viii Contents Part III Deep Learning 287 13 Neural Network 289 13.1 From One Layer to Multiple Layers 13.2 Building a Text Classifierwithout Manual Features 13.3 Improving Neural Network Training Summary Chapter Notes Exercises 14 Representation Learning 14.1 Recurrent Neural Network 14.2 Neural Attention 14.3 Representing Trees 14.4 Representing Graphs 14.5 Analysing Representation 14.6 More on Neural Network Training Summary Chapter Notes Exercises 15 Neural Structured Prediction 15.1 Local Graph-Based Models 15.2 Local Transition-Based Models 15.3 Global Structured Models Summary Chapter Notes Exercises 16 Working with Two Texts 16.1 Sequence-to-Sequence Models 16.2 Text Matching Models Summary Chapter Notes Exercises 17 Pre-training and Transfer Learning 17.1 17.2 17.3 Neural Language Models and Word Embedding Contextualised Word Representations Transfer Learning 289 300 306 310 310 311 314 314 320 325 329 333 334 338 338 339 343 343 351 358 367 367 367 370 370 382 392 392 393 396 396 409 414
Contents Summary Chapter Notes Exercises 18 Deep Latent Variable Models 18.1 Introducing Latent Variables into a Neural Network Model 18.2 Working with Categorical Latent Variables 18.3 Working with Structured Latent Variables 18.4 Variational Inference 18.5 Neural Topic Models 18.6 VAEs for Language Modelling Summary Chapter Notes Exercises Bibliography Index IX 419 420 420 423 423 424 429 434 443 444 450 450 450 453 468
An amazingly compact, and at the same time comprehensive, introduction and reference to natural language processing (NLP). It describes the NLP basics, then employs this knowledge to solve typical NLP problems. It achieves very high coverage of NLP through a clever abstraction to typical high-level tasks, such as sequence labelling. Finally, it explains the topics in deep learning. The book captivates through its simple elegance, depth, and accessibility to a wide range of readers from undergrads to experienced researchers. Iryna Gurevych, Technical University of Darmstadt, Germany An excellent introduction to the field of natural language processing including recent advances in deep learning. By organising the material in terms of machine learning techniques - instead of the more traditional division by linguistic levels or applications - the authors are able to discuss different topics within a single coherent framework, with a gradual progression from basic notions to more complex material. Joakim Nivre, Uppsala University 1 |
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| spelling | Zhang, Yue (DE-588)1236110978 aut Natural language processing a machine learning perspective Yue Zhang (Westlake University), Zhiyang Teng (Westlake University) Cambridge ; New York ; Melbourne ; New Delhi ; Singapore Cambridge University Press 2021 xvi, 470 Seiten Diagramme txt rdacontent n rdamedia nc rdacarrier Computerlinguistik (DE-588)4035843-4 gnd rswk-swf Natürliche Sprache (DE-588)4041354-8 gnd rswk-swf Natürliche Sprache (DE-588)4041354-8 s Computerlinguistik (DE-588)4035843-4 s DE-604 Teng, Zhiyang Verfasser (DE-588)1236110986 aut Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032623612&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032623612&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | Zhang, Yue Teng, Zhiyang Natural language processing a machine learning perspective Computerlinguistik (DE-588)4035843-4 gnd Natürliche Sprache (DE-588)4041354-8 gnd |
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| title | Natural language processing a machine learning perspective |
| title_auth | Natural language processing a machine learning perspective |
| title_exact_search | Natural language processing a machine learning perspective |
| title_exact_search_txtP | Natural language processing a machine learning perspective |
| title_full | Natural language processing a machine learning perspective Yue Zhang (Westlake University), Zhiyang Teng (Westlake University) |
| title_fullStr | Natural language processing a machine learning perspective Yue Zhang (Westlake University), Zhiyang Teng (Westlake University) |
| title_full_unstemmed | Natural language processing a machine learning perspective Yue Zhang (Westlake University), Zhiyang Teng (Westlake University) |
| title_short | Natural language processing |
| title_sort | natural language processing a machine learning perspective |
| title_sub | a machine learning perspective |
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