文本挖掘经典教材 清晰PDF

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文本挖掘是数绝挖掘比较新的方向，在计算机快速发展的今天，文本挖掘的意义尤为重要
该书是文本挖掘的奠基之作
如果大家对文本挖掘感兴趣，看懂这一本书足够
建议大家先看看书的各章目录，这样对全书有一个全面的认识
不一定要从第一章开始看

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关键词：经典教材 文本挖掘 PDF 第一章 看看书 经典 教材 PDF 挖掘 文本

Contents
Preface page x
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 for
Text 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

全书423页
非常清晰

谢谢啦，！！！

这本是不错的，记得前年的时候，导师专门到国家图书馆去复印的

搂主有没有关于文本聚类分析的比较系统的资料？

thanks for sharing!

谢谢，支持楼主

价格不菲呀。

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