Table of contents for The text mining handbook : advanced approaches in analyzing unstructured data / Ronen Feldman, James Sanger.

Bibliographic record and links to related information available from the Library of Congress catalog.

Note: Contents data are machine generated based on pre-publication provided by the publisher. Contents may have variations from the printed book or be incomplete or contain other coding.

I. Introduction to Text Mining
I.1 Defining Text Mining
I.2 General Architecture of Text Mining Systems
II. Core Text Mining Operations
II.1 Core Text Mining Operations
II.2 Using Background Knowledge for Text Mining
II.3 Text Mining Query Languages
III. Text Mining Preprocessing Techniques
III.1 Task-Oriented Approaches III.2 Further
IV. Categorization
IV.1 Applications of Text Categorization
V. Clustering
V.1 Clustering Tasks in Text Analysis
VI. Information Extraction
VI.1 Introduction to Information Extraction
VI.2 Historical Evolution of IE: the Message Understanding
Conferences and Tipster
VI.3 IE Examples
VI.4 Architecture of IE Systems
VI.5 Anaphora Resolution
VI.6 Inductive Algorithm for IE
VI.7 Structured IE
VI.8 Further Reading
VII. Probabilistic Models for Information Extraction
VII.1 Hidden Markov Models
VII.2 Stochastic Context-Free Grammars
VII.3 Maximal Entropy Modeling
VII.4 Maximal Entropy Markov Models
VII.5 Conditional Random Fields
VII.6 Further Reading
VIII Preprocessing Applications Using Probabilistic and Hybrid Ap
proaches VIII.1 Applications of HMM to Textual Analysis VIII.2
Using MEMM for Information Extraction VIII.3 Applications of
CRFs to Textual Analysis VIII.4 TEG: Using SCFG Rules for Hybrid
Statistical/ Knowledge-Based IE VIII.5 Bootstrapping VIII.6 Further
IX Presentation-Layer Considerations for Browsing and Query Re
finement IX.1 Browsing IX.2 Assessing Constraints and Simple
Specification Filters at the Presentation Layer IX.3 Assessing the
Underlying Query Language
X Visualization Approaches
X.1 Introduction
X.2 Architectural Considerations
X.3 Common Visualization Approaches for Text Mining
X.4 Visualization Technique in Link Analysis
X.5 Real World Example: The Document Explorer System
XI Link Analysis
XI.1 Preliminaries
XI.2 Automatic Layout of Networks
XI.3 Paths and Cycles in Graphs
XI.4 Centrality
XI.5 Partitioning of Networks
XI.6 Pattern Matching in Networks
XI.7 Software Packages for Link Analysis
XII Text Mining Applications
XII.1 General Considerations
XII.2 Corporate Finance: Mining Industry Literature for Busi-
ness Intelligence
XII.3 A "Horizontal" Text Mining Application: Patent Analysis
Solution Leveraging a Commercial Text Analytics Platform
XII.4 Life Sciences Research: Mining Biological Pathway In-
formation with Geneways
Appendix: Dial: A Dedicated Information Extraction Language for
Text Mining
1. Introduction to DIAN
2. Information Extraction in the DIAL Environment
3. Text Tokenization
4. Concepts and Rule Structure
5. Pattern Matching
6. Rule Constraints
7. Concept Guards
8. Actions
9. Inheritance
10. Complete Examples

Library of Congress Subject Headings for this publication:

Data mining -- Handbooks, manuals, etc.