كتاب Text Mining with MATLAB

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أحضرت لكم كتاب
Text Mining with MATLAB
Second Edition
Rafael E. Banchs

و المحتوى كما يلي :

Table of Contents
1 Introduction 1
1.1 About Text Mining and MATLAB .1
1.2 About this Book 3
1.3 A (very) Brief Introduction to MATLAB .6
1.4 The Text Analytics Toolbox 11
1.5 Further Reading .13
1.6 References .13
PART I: FUNDAMENTALS 15
2 Handling Text Data 17
2.1 Character and Character Arrays 17
2.2 Handling Text with Cell Arrays 20
2.3 Handling Text with Structures 23
2.4 Handling Text with String Arrays .26
2.5 Some Useful Functions .29
2.6 Further Reading .33
2.7 Proposed Exercises 33
2.8 References .35
3 Regular Expressions .37
3.1 Basic Operators for Matching Characters .37
3.2 Matching Sequences of Characters .40
3.3 Conditional Matching 43
3.4 Working with Tokens 45
3.5 Further Reading .48
3.6 Proposed Exercises 48
3.7 References .51
4 Basic Operations with Strings .53
4.1 Searching and Comparing .53
4.2 Replacement and Insertion 61
4.3 Segmentation and Concatenation 64
4.4 Set Operations .71
4.5 Further Reading .77
4.6 Proposed Exercises 77
4.7 References .80
ix5 Reading and Writing Files . 81
5.1 Basic File Formats 81
5.2 Other Useful Formats 90
5.3 Handling Files and Directories 106
5.4 Further Reading . 113
5.5 Proposed Exercises . 114
5.6 References . 117
6 The Structure of Language 119
6.1 Levels of the Linguistic Phenomena . 119
6.2 Morphology and Syntax 122
6.3 Semantics and Pragmatics . 131
6.4 Further Reading . 137
6.5 Proposed Exercises . 137
6.6 References . 140
PART II: MATHEMATICAL MODELS . 143
7 Basic Corpus Statistics . 145
7.1 Fundamental Properties . 145
7.2 Word Co-occurrences . 158
7.3 Accounting for Order 165
7.4 Further Reading . 170
7.5 Proposed Exercises . 171
7.6 Short Projects 173
7.7 References . 175
8 Statistical Models 177
8.1 Basic n-gram Models 177
8.2 Discounting . 180
8.3 Model Interpolation . 188
8.4 Topic Models 192
8.5 Further Reading . 204
8.6 Proposed Exercises . 205
8.7 Short Projects 207
8.8 References . 209
9 Geometrical Models . 211
9.1 The Term-Document Matrix . 211
9.2 The Vector Space Model . 219
9.3 Association Scores and Distances . 228
9.4 Further Reading . 235
9.5 Proposed Exercises . 235
9.6 Short Projects 238
9.7 References . 239
x Contents10 Dimensionality Reduction 241
10.1 Vocabulary Pruning and Merging .241
10.2 The Linear Transformation Approach .247
10.3 Non-linear Projection Methods .258
10.4 Embeddings .264
10.5 Further Reading .273
10.6 Proposed Exercises 274
10.7 Short Projects 276
10.8 References .277
PART III: METHODS AND APPLICATIONS .279
11 Document Categorization 281
11.1 Data Collection Preparation 281
11.2 Unsupervised Clustering .287
11.3 Supervised Classification in Vector Space 294
11.4 Supervised Classification in Probability Space .308
11.5 Further Reading .317
11.6 Proposed Exercises 318
11.7 Short Projects 322
11.8 References .324
12 Document Search 327
12.1 Binary Search 327
12.2 Vector-based Search 338
12.3 The BM25 Ranking Function 344
12.4 Cross-language Search 347
12.5 Further Reading .358
12.6 Proposed Exercises 359
12.7 Short Projects 361
12.8 References .362
13 Content Analysis .365
13.1 Dimensions of Analysis 365
13.2 Polarity Estimation 370
13.3 Qualifier and Aspect Identification .379
13.4 Entity, Relation and Definition Extraction 389
13.5 Further Reading .398
13.6 Proposed Exercises 399
13.7 Short Projects 403
13.8 References .404
Contents xi14 Keyword Extraction and Summarization 407
14.1 Keywords and Word Clouds . 407
14.2 Text Summarization 420
14.3 Further Reading . 429
14.4 Proposed Exercises . 430
14.5 Short Projects 432
14.6 References . 434
15 Question Answering and Dialogue 435
15.1 Question Answering 435
15.2 Dialogue Systems 450
15.3 Further Reading . 466
15.4 Proposed Exercises . 466
15.5 Short Projects 472
15.6 References
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