Computational analysis and understanding of natural languages: principles, methods and applications
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| Other Authors: | |
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| Format: | Book |
| Language: | English |
| Published: |
Amsterdam
Elsevier, North Holland
[2018]
|
| Series: | Handbook of statistics
volume 38 |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | xxi, 515 Seiten Diagramme 24 cm |
| ISBN: | 9780444640420 |
Staff View
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| adam_text | Contents
Contributors xv
Preface xvii
Part A
Linguistic Principles and Computational Resources
1. Linguistics: Core Concepts and Principles 3
Akhil Gudivada, Dhana L. Rao, and Venkat N. Gudivada
1 Introduction 3
1.1 Chapter Organization 4
2 Subfields of Linguistics 4
3 Variation in Languages 7
4 Phonetics 10
5 Phonology 11
6 Morphology 11
7 Syntax 12
8 Semantics 13
9 Summary 13
Acknowledgment 13
References 14
2. Languages and Grammar 15
Akhil Gudivada and Dhana L Rao
1 Introduction 15
1.1 Three Aspects of Languages 17
1.2 Chapter Organization 17
2 Formal Grammars 17
3 Grammar Classes and Corresponding Languages 1 8
3.1 Regular Languages 19
3.2 Context-Free Languages 20
3.3 Parse Trees 23
3.4 Context-Sensitive Languages 26
3.5 Recursively Enumerable and Recursive Languages 27
vi Contents
4 A Simplistic Context-Free Grammar for English Language 27
5 Summary 28
References 28
3. Open-Source Libraries, Application Frameworks,
and Workflow Systems for NLP 31
Venkat N. Gudivada and Kamyar Arbabifard
1 Introduction 31
2 Corpus Datasets 32
2.1 Corpus Tools 32
3 NLP Datasets 34
4 Treebanks 36
5 Software Libraries and Frameworks for Machine Learning 36
5.1 TensorFfow 36
5.2 Deep Learning for Java 37
5.3 Apache MXNet 37
5.4 The Microsoft Cognitive Toolkit 37
5.5 Keras 37
5.6 Torch and PyTorch 37
5.7 Scikit-Learn 38
5.8 Caffe 38
5.9 Accord.NET 38
5.10 Spark MLlib 38
6 Software Libraries and Frameworks for NLP 38
6.1 Natural Language Toolkit 38
6.2 Stanford CoreNLP Toolset 39
6.3 Apache OpenNLP 39
6.4 General Architecture for Text Engineering 39
6.5 Machine Learning for Language Toolkit 39
6.6 Tools for Social Media NLP 39
7 Task-Specific NLP Tools 40
7.1 Language Identification 40
7.2 Sentence Segmentation 40
7.3 Word Segmentation 41
7.4 Part-of-Speech Tagging 42
7.5 Parsing 42
7.6 Named Entity Recognition 43
7.7 Semantic Role Labeling 43
7.8 Information Extraction 44
7.9 Machine Translation 45
7.10 Topic Modeling 46
8 Workflow Systems 46
9 Conclusions 48
Acknowledgment 48
References 48
Contents vii
Part B
Mathematical and Machine Learning Foundations
4. Mathematical Essentials 53
China Venkaiah Vacflamudi and Sesha Phani Deepika
Vadiamudi
1 Introduction 53
1.1 Functions 53
1.2 Linear Algebra 53
1.3 Information Theory 54
1.4 Optimization 54
2 Functions 54
2.1 Operations 57
3 Linear Algebra 58
3.1 Vector Spaces and Subspaces 58
3.2 Linearly Independent Sets and Bases 58
3.3 Dimension of a Vector Space 61
3.4 Orthogonality 61
3.5 Linear Transformations and Change of Bases 62
3.6 Eigenvalues and Eigen-Vectors 65
4 Information Theory 67
4.1 Self-Information 67
4.2 Average Self-Information 68
4.3 Conditional Self-Information 68
4.4 Mutual Information 68
4.5 Entropy 69
4.6 Joint Entropy 69
4.7 Conditional Entropy 70
4.8 Average Mutual Information 72
5 Optimization 72
Further Reading 73
5. Probability Essentials 75
Paul Vos and Qiang Wu
1 Preliminaries 75
2 Formal Definitions 76
3 Conditional Probability 79
4 Bayes Theorem 80
5 M-Valued Random Variables 82
5.1 Probability Distributions 83
5.2 Expectation and Variance 88
5.3 Some Common Discrete Random Variables 90
5.4 Some Common Continuous Distributions 93
95
95
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6 Rn-Valued Random Variables
6.1 Joint Distributions
6.2 Expectation and Covariance
6.3 Conditional Distributions and Bayes Theorem
7 Independent Random Variables
8 Central Limit Theorem
References
Inference and Prediction
Qiang Wu and Paul Vos
1 Introduction
2 Notation
3 Sufficient Statistics
4 Likelihood Principle
5 Point Estimation
5.1 Method of Moments Estimator
5.2 Maximum Likelihood Estimator
5.3 Iterative Algorithms for MLE
5.4 Finite Sample Properties
5.5 Asymptotic Properties
5.6 Bootstrap and Jackknife Resampling
5.7 Robust Estimators
6 Hypothesis Testing
6.1 Likelihood Ratio Test
6.2 Other Large Sample Tests
6.3 Power Function and Decision Making
6.4 Two-Sample Comparisons
7 Interval Estimation
7.1 Finding Interval Estimators
7.2 Evaluating Interval Estimators
8 Bayesian Methods
8.1 Prior and Posterior Distributions
8.2 Improper Prior
8.3 Bayesian Estimation
8.4 Bayesian Hypothesis Testing
8.5 Bayesian Prediction
8.6 Bayes Sampling Methods
9 Prediction and Model Selection
9.1 Model Selection
9.2 Cross-Validation
9.3 From Bagging to Random Forests
References
Bayesian Methods
Indranil Ghosh
1 Bayesian Methods
1.1 Bayes Rule: Discrete Case
1.2 Bayes Rule: Continuous Case
Contents
IX
2 Bayesian Networks 176
2.1 inference in Bayesian Networks 177
2.2 Bayesian Parameter Estimation 179
3 Conceptual Exercises 180
4 Markov Networks 182
4.1 Discrete Markov Network 183
5 Inference in Markov Networks 184
5.1 Inference as Optimization 186
5.2 Sampling-Based Approximate inference 187
5.3 Markov Chain Monte Carlo Methods 188
5.4 Markov Chains for Graphical Models 189
5.5 Gibbs Sampling 190
5.6 Parameter Estimation in Markov Networks 191
6 Conceptual Exercises 192
7 Summary 193
Acknowledgments 194
References 195
8. Machine Learning 197
Gangadhar Shobha and Shanta Rangaswamy
1 Introduction to Machine Learning 197
1.1 Supervised Learning 198
1.2 Unsupervised Learning 200
1.3 Semi-supervised Learning 200
1.4 Reinforcement Learning 200
2 Terminologies 200
3 Regularization and Bias-Variance Trade-Off 201
4 Evaluating Machine Learning Algorithms 203
4.1 Accuracy 203
4.2 Confusion Matrix 203
4.3 Precision and Recall 204
4.4 F Measure 204
4.5 Regression Metrics 204
4.6 k-Fold Cross-validation 205
4.7 Stratified Ar-Fold Cross-validation 206
4.8 Advantage and Disadvantage of Cross-validation 206
4.9 Bootstrapping and Bagging 207
5 Regression Algorithms 207
6 Classification Algorithms 208
6.1 Decision Tree Algorithm 208
6.2 Naive Bayesian Classification 215
6.3 Support Vector Machine 216
7 Clustering Algorithms 222
7.1 /r-Means Clustering 223
7.2 Hierarchical Clustering 225
8 Applications 226
9 Conclusion 226
9.1 Challenges and Opportunities 227
Further Reading 227
X
9. Deep Neural Networks for Natural Language
Processing
Ehsan Fathi and Babak Maleki Shoja
1 Introduction
2 Word Vectors Representations
3 Feedforward Neural Networks
3.1 Neural Networks
4 Training Deep Models and Optimization
4.1 Hidden Units
4.2 Backpropagation
5 Regularization for Deep Learning
5.1 Parameter Norm Penalties
5.2 Sparse Representation
6 Sequence Modeling (Language Modeling)
6.1 Count-Based Models or n-Grams
6.2 Recurrent Neural Networks Language Models
6.3 Bidirectional Neural Networks
6.4 Vanishing and Exploding Gradient Problem
6.5 The Long Short-Term Memory and Gated RNNs
6.6 Encoder-Decoder Sequence-to-Sequence Architectures
6.7 Recursive Neural Networks
7 Convolutional Neural Networks
7.1 What is Convolution?
7.2 Tricks to Improve the Performance
7.3 Narrow vs Wide Convolution
7.4 Stride Size
7.5 Application of CNN as Input for RNN
8 Memory
8.1 Attention (ROM)
8.2 Register Machines (RAM)
8.3 Neural Pushdown Automata
9 Summary
References
10. Deep Learning for Natural Language Processing
Ying Xie, Linh Le, Yiyun Zhou, and Vijay V. Raghavan
1 Introduction
2 Survey of Deep Learning Techniques on NLP
3 Sentence Embedding Based on SOM
3.1 The Method
3.2 Experiments
4 Representing, Visualizing, and Processing Documents
as Images
5 Discussion and Conclusion
References
Further Reading
Contents
229
229
231
251
254
256
257
260
263
264
270
273
273
275
279
280
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284
285
288
289
293
295
296
296
296
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308
310
314
314
317
317
318
320
320
322
323
326
327
328
Contents xi
Part C
Applications and Linguistic Diversity
11. Information Retrieval: Concepts, Models, and
Systems 331
Venkat N. Gucfivada, Dhana L. Rao,
and Amogh R. Gudivada
1 Introduction 331
1.1 Chapter Organization 333
2 A Reference Architecture for Current IR Systems 334
3 Document Preprocessing 335
3.1 Document Granularity 335
3.2 Tokenization 336
3.3 Stemming and Lemmatization 337
3.4 Stop Words, Accents, Case Folding, and Language
Identification 338
4 Mini Gutenberg Text Corpus 338
4.1 Distribution of Characters 340
4.2 Unigrams, Bigrams, and Trigrams 340
4.3 Zip s Law 341
5 A Categorization of IR Models 342
6 Boolean IR Model 346
7 Positional Index, Phrase, and Proximity Queries 350
7.1 Processing Boolean Queries Using the Positional
Inverted index 353
7.2 Processing Phrase Queries Using the Positional
Inverted Index 353
7.3 Processing Proximity Queries Using the Positional
Inverted Index 354
7.4 Recovering Document Source Text Using the Positional
Inverted Index 354
8 Term Weighting 355
8.1 Log Frequency Term Weighting 355
8.2 tf-idf Weighting 356
8.3 Term Discrimination Value 359
8.4 Document Length Normalization 359
8.5 BM25 Term Weighting 361
9 Vector Space IR Model 362
10 Probabilistic IR Models 364
10.1 Binary Independence Model 364
1 0.2 Okapi BM25 Model 366
11 Language Model-Based IR 368
11.1 Statistical Language Modeling 369
11.2 The Query Likelihood Model 370
12 Evaluating IR Systems 374
12.1 Precision and Recall for Unranked Retrieval 375
12.2 The F-measure 376
12.3 Retrieval Effectiveness for Ranked Retrieval 377
XII
Contents
12.4 Precision-Recalf Graphs 379
12.5 Reciprocal Rank 379
12.6 Discounted Cumulative Gain 380
12.7 Eliciting Relevance Judgments Using Pooling 382
13 Relevance Feedback and Query Expansion 382
13.1 Modifying Query Representation 382
13.2 Modifying Document Representation 383
13.3 Pseudo-Relevance Feedback 383
13.4 Theoretical Optimal Query: Rocchio s Algorithm 385
14 IR Libraries, Frameworks, and Test Collections 386
14.1 Solr and Elasticsearch 386
14.2 Lucene Image Retrieval 386
14.3 Apache UIMA® 387
14.4 Lemur and Wumpus 387
14.5 NLP/IR Tools 387
14.6 Test Collections 388
14.7 TREC Collections 388
15 Facets of IR Research 389
15.1 Vocabulary, Faceted, and Exploratory Search 389
15.2 Information Architecture 389
15.3 Search Interfaces and User Modeling 390
15.4 Personal Information Management 390
15.5 Neural Network Approaches to IR 390
15.6 Query Difficulty 390
15.7 information Extraction 391
15.8 Text Simplification 391
15.9 Machine Translation-Based Approaches to IR 391
15.10 XML Retrieval 391
15.11 Dynamic Information Retrieval 391
15.12 Metasearch Engines 392
15.13 Scholarly Collaboration Using Academic Social Web
Platforms 392
15.14 Access Control 392
15.15 Multimedia and Cross-Language Retrieval 392
15.16 Long-Range IR Challenges and Opportunities 392
16 Additional Reading 393
16.1 Earliest Books 393
16.2 Recent Books 393
16.3 Machine Learning and NLP 394
16.4 Journals and Conferences 394
Acknowledgments 395
References 395
12. Natural Language Core Tasks and Applications 403
Venkat N. Gudivada
1 Introduction 403
1.1 Chapter Organization 404
2 Annotated Language Corpora 405
Contents
• • •
XIII
3 Language Identification 405
4 Text and Word Segmentation 406
5 Word-Sense Disambiguation (WSD) 407
6 Language Modeling 408
7 PoS Tagging 410
7.1 Generative and Noisy-Channel Models 411
7.2 Multilayer Perceptron Neural Network Model for PoS 412
8 Parsing 413
9 Named Entity Recognition 414
10 Machine Translation 415
11 Information Extraction 417
12 Text Summarization 41 7
13 Question-Answering Systems 418
14 Natural Language User Interfaces 419
15 Summary 421
Acknowledgments 421
References 421
13. Linguistic Elegance of the Languages of South India 429
Deepamala Nijagunappa
1 Introduction 429
2 History and Evolution of Dravidian Languages 430
2.1 History of Indo-European Languages 430
2.2 Brahmi Script 431
2.3 Sanskrit Language 432
2.4 Dravidian Languages 435
3 Linguistic Elegance and Language Traditions of South Indian
Languages 436
3.1 Telugu 436
3.2 Kannada 440
3.3 Tamil 447
3.4 Malayalam 449
4 Classical Languages of India 449
4.1 Classics in Sanskrit 452
4.2 Classics in Telugu 453
4.3 Classics in Kannada 454
4.4 Classics in Tamil 455
4.5 Classics in Malayalam 456
4.6 Classics in Odiya 457
5 influence of Other Languages on South Indian Languages 457
5.1 Propagation of Hindi in India 457
5.2 English as a Medium of Communication 458
5.3 Impact of Globalization 459
5.4 Symbiosis Between English and South Indian Languages 460
5.5 Promoting South Indian Languages 460
6 Summary 461
References 461
Contents
Text Mining for Modeling Cyberattacks 463
Steven Noel
1 Introduction 463
2 Anatomy of an Attack Pattern 465
3 Applying Attack Patterns to Scenarios 468
3.1 Resource Consumption Attacks 469
3.2 Attacks for Introduction-Based Routing 470
4 Mining Attack Pattern Text 478
4.1 Vector-Space Attack Pattern Model 478
4.2 Query Relevance Distance 480
4.3 Attack Pattern Distances 482
4.4 Attack Pattern Clustering 484
5 Attack Chains 499
6 Attack Pattern Hierarchies 504
7 Analytic Environment 510
8 Summary 512
References 513
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| indexdate | 2024-07-10T08:05:42Z |
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| record_format | marc |
| series | Handbook of statistics |
| series2 | Handbook of statistics |
| spelling | Computational analysis and understanding of natural languages principles, methods and applications edited by Venkat N. Gudivada, C.R. Rao Amsterdam Elsevier, North Holland [2018] © 2018 xxi, 515 Seiten Diagramme 24 cm txt rdacontent n rdamedia nc rdacarrier Handbook of statistics volume 38 Verstehen (DE-588)4063241-6 gnd rswk-swf Computerlinguistik (DE-588)4035843-4 gnd rswk-swf Analysis (DE-588)4001865-9 gnd rswk-swf Natürliche Sprache (DE-588)4041354-8 gnd rswk-swf (DE-588)4143413-4 Aufsatzsammlung gnd-content Computerlinguistik (DE-588)4035843-4 s Natürliche Sprache (DE-588)4041354-8 s DE-604 Analysis (DE-588)4001865-9 s Verstehen (DE-588)4063241-6 s Gudivada, Venkat N. (DE-588)179973681 edt ctb Handbook of statistics volume 38 (DE-604)BV000002510 38 Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030347598&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Computational analysis and understanding of natural languages principles, methods and applications Handbook of statistics Verstehen (DE-588)4063241-6 gnd Computerlinguistik (DE-588)4035843-4 gnd Analysis (DE-588)4001865-9 gnd Natürliche Sprache (DE-588)4041354-8 gnd |
| subject_GND | (DE-588)4063241-6 (DE-588)4035843-4 (DE-588)4001865-9 (DE-588)4041354-8 (DE-588)4143413-4 |
| title | Computational analysis and understanding of natural languages principles, methods and applications |
| title_auth | Computational analysis and understanding of natural languages principles, methods and applications |
| title_exact_search | Computational analysis and understanding of natural languages principles, methods and applications |
| title_full | Computational analysis and understanding of natural languages principles, methods and applications edited by Venkat N. Gudivada, C.R. Rao |
| title_fullStr | Computational analysis and understanding of natural languages principles, methods and applications edited by Venkat N. Gudivada, C.R. Rao |
| title_full_unstemmed | Computational analysis and understanding of natural languages principles, methods and applications edited by Venkat N. Gudivada, C.R. Rao |
| title_short | Computational analysis and understanding of natural languages |
| title_sort | computational analysis and understanding of natural languages principles methods and applications |
| title_sub | principles, methods and applications |
| topic | Verstehen (DE-588)4063241-6 gnd Computerlinguistik (DE-588)4035843-4 gnd Analysis (DE-588)4001865-9 gnd Natürliche Sprache (DE-588)4041354-8 gnd |
| topic_facet | Verstehen Computerlinguistik Analysis Natürliche Sprache Aufsatzsammlung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030347598&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV000002510 |
| work_keys_str_mv | AT gudivadavenkatn computationalanalysisandunderstandingofnaturallanguagesprinciplesmethodsandapplications |