文本挖掘

I. Introduction to Text Mining 1

I.1 Defining Text Mining 1

I.2 General Architecture of Text Mining Systems 13

II. Core Text Mining Operations 19

II.1 Core Text Mining Operations 19

II.2 Using Background Knowledge for Text Mining 41

II.3 Text Mining Query Languages 51

III. Text Mining Preprocessing Techniques 57

III.1 Task-Oriented Approaches 58

III.2 Further Reading 62

IV. Categorization 64

IV.1 Applications of Text Categorization 65

IV.2 Definition of the Problem 66

IV.3 Document Representation 68

IV.4 Knowledge Engineering Approach to TC 70

IV.5 Machine Learning Approach to TC 70

IV.6 Using Unlabeled Data to Improve Classification 78

IV.7 Evaluation of Text Classifiers 79

IV.8 Citations and Notes 80

V. Clustering 82

V.1 Clustering Tasks in Text Analysis 82

V.2 The General Clustering Problem 84

V.3 Clustering Algorithms 85

V.4 Clustering of Textual Data 88

V.5 Citations and Notes 92

VI. Information Extraction 94

VI.1 Introduction to Information Extraction 94

VI.2 Historical Evolution of IE： The Message Understanding Conferences and Tipster 96

VI.3 IE Examples 101

VI.4 Architecture of IE Systems 104

VI.5 Anaphora Resolution 109

VI.6 Inductive Algorithms for IE 119

VI.7 Structural IE 122

VI.8 Further Reading 129

VII. Probabilistic Models for Information Extraction 131

VII.1 Hidden Markov Models 131

VII.2 Stochastic Context-Free Grammars 137

VII.3 Maximal Entropy Modeling 138

VII.4 Maximal Entropy Markov Models 140

VII.5 Conditional Random Fields 142

VII.6 Further Reading 145

VIII. Preprocessing Applications Using Probabilistic and Hybrid Approaches 146

VIII.1 Applications of HMM to Textual Analysis 146

VIII.2 Using MEMM for Information Extraction 152

VIII.3 Applications of CRFs to Textual Analysis 153

VIII.4 TEG： Using SCFG Rules for Hybrid Statistical–Knowledge-Based IE 155

VIII.5 Bootstrapping 166

VIII.6 Further Reading 175

IX. Presentation-Layer Considerations for Browsing and Query Refinement 177

IX.1 Browsing 177

IX.2 Accessing Constraints and Simple Specification Filters at the Presentation Layer 185

IX.3 Accessing the Underlying Query Language 186

IX.4 Citations and Notes 187

X. Visualization Approaches 189

X.1 Introduction 189

X.2 Architectural Considerations 192

X.3 Common Visualization Approaches for Text Mining 194

X.4 Visualization Techniques in Link Analysis 225

X.5 Real-World Example： The Document Explorer System 235

XI. Link Analysis 244

XI.1 Preliminaries 244

XI.2 Automatic Layout of Networks 246

XI.3 Paths and Cycles in Graphs 250

XI.4 Centrality 251

XI.5 Partitioning of Networks 259

XI.6 Pattern Matching in Networks 272

XI.7 Software Packages for Link Analysis 273

XI.8 Citations and Notes 274

XII. Text Mining Applications 275

XII.1 General Considerations 276

XII.2 Corporate Finance： Mining Industry Literature for Business Intelligence 281

XII.3 A “Horizontal” Text Mining Application： Patent Analysis Solution Leveraging a Commercial Text Analytics Platform 297

XII.4 Life Sciences Research： Mining Biological Pathway Information with GeneWays 309

Appendix A： DIAL： A Dedicated Information Extraction Language forText Mining 317

A.1 What Is the DIAL Language? 317

A.2 Information Extraction in the DIAL Environment 318

A.3 Text Tokenization 320

A.4 Concept and Rule Structure 320

A.5 Pattern Matching 322

A.6 Pattern Elements 323

A.7 Rule Constraints 327

A.8 Concept Guards 328

A.9 Complete DIAL Examples 329

Bibliography 337

Index 391

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