The Text Mining Handbook

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	42
	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	226
	X.5 Real-World Example: The Document Explorer System	237
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

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Library of Congress

Advanced Approaches in Analyzing Unstructured Data

Ronen Feldman

Hebrew University of Jerusalem

James Sanger

ABS Ventures, Boston, Massachusetts

Hardback

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