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文本挖掘（英文版）
作者：（以）费尔德曼，（美）桑格 著
出版社：人民邮电出版社
出版时间：2009-08-01
ISBN：9787115205353
定价：¥69.00
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内容简介
《文本挖掘（英文版）》是一部文本挖掘领域名著，作者为世界知名的权威学者。书中涵盖了核心文本挖掘操作、文本挖掘预处理技术、分类、聚类、信息提取、信息提取的概率模型、预处理应用、可视化方法、链接分析、文本挖掘应用等内容，很好地结合了文本挖掘的理论和实践。《文本挖掘（英文版）》非常适合文本挖掘、信息检索领域的研究人员和实践者阅读，也适合作为高等院校计算机及相关专业研究生的数据挖掘和知识发现等课程的教材。
作者简介
Ronen FeIdmarl，机器学习、数据挖掘和非结构化数据管理的先驱人物。以色列Bar一liarl大学数学与计算机科学系高级讲师、数据挖掘实验室主任，Clearforest公司（主要为企业和政府机构开发下一代文本挖掘应用）合作创始人、董事长，现在还是纽约大学Stern商学院的副教授。James Sanger风险投资家，商业数据解决方案、因特网应用和IT安全产品领域公认的行业专家。他于1982年与人合伙创立了ABS Vetllures公司。此前，他是DB Capital纽约公司的常务董事他本科毕业于宾夕法尼亚大学，研究生就读于牛津大学和利物浦大学他是IEEE和美国人工智能协会（AAAI）会员。
目录
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
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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