这是一本由该领域最重要专家编写的文本挖掘导论。这本书写得非常好。它完美地结合了文本挖掘的理论和实践，既适合研究人员又适合实践者……极力推荐那些没有任何计算语言学背景而想钻研文本挖掘领域的人阅读本书。

本书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。

本书非常适合文本挖掘、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。

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 Reading

Ⅳ. Categorization

　Ⅳ.1　Applications of Text Categorization

　Ⅳ.2　Definition of the Problem

　Ⅳ.3　Document Representation

　Ⅳ.4　Knowledge Engineering Approach to TC

　Ⅳ.5　Machine Learning Approach to TC

　Ⅳ.6　Using Unlabeled Data to Improve Classification

　Ⅳ.7　Evaluation of Text Classifiers

　Ⅳ.8　Citations and Notes

Ⅴ. Clustering

　Ⅴ.1　Clustering Tasks in Text Analysis

　Ⅴ.2　The General Clustering Problem

　Ⅴ.3　Clustering Algorithms

　Ⅴ.4　Clustering of Textual Data

　Ⅴ.5　Citations and Notes

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 Algorithms for IE

　Ⅵ.7　Structural 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 Approaches

　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 Reading

Ⅸ. Presentation-Layer Considerations for Browsing and Query Refinement

　Ⅸ.1　Browsing

　Ⅸ.2　Accessing Constraints and Simple Specification Filters at the Presentation Layer

　Ⅸ.3　Accessing the Underlying Query Language

　Ⅸ.4　Citations and Notes

Ⅹ. Visualization Approaches

　Ⅹ.1　Introduction

　Ⅹ.2　Architectural Considerations

　Ⅹ.3　Common Visualization Approaches for Text Mining

　Ⅹ.4　Visualization Techniques 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

　Ⅺ.8　Citations and Notes

Ⅻ. Text Mining Applications

　Ⅻ.1　General Considerations

　Ⅻ.2　Corporate Finance: Mining Industry Literature for Business 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 Information with GeneWays

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

　A.1　What Is the DIAL Language?

　A.2　Information Extraction in the DIAL Environment

　A.3　Text Tokenization

　A.4　Concept and Rule Structure

　A.5　Pattern Matching

　A.6　Pattern Elements

　A.7　Rule Constraints

　A.8　Concept Guards

　A.9　Complete DIAL Examples

Bibliography

Index