Text Analytics: An Introduction to the Science and Applications of Unstructured Information Analysis

Cover
Half Title
Title Page
Copyright Page
Dedication
Table of Contents
List of Figures
List of Tables
Preface
Acknowledgments
Author
Chapter 1 Text Analytics
    1.1 Introduction
    1.2 Text Mining and Text Analytics
    1.3 Tasks and Applications
    1.4 The Text Analytics Process
    1.5 Summary
    1.6 Questions
Chapter 2 Natural-Language Processing
    2.1 Introduction
    2.2 The Scope of Natural-Language Processing
    2.3 NLP Levels and Tasks
        2.3.1 Phonology
        2.3.2 Morphology
        2.3.3 Lexicon
        2.3.4 Syntax
        2.3.5 Semantics
        2.3.6 Reasoning and Pragmatics
    2.4 Summary
    2.5 Exercises
        2.5.1 Morphological Analysis
        2.5.2 Lexical Analysis
        2.5.3 Syntactic Analysis
Chapter 3 Information Extraction
    3.1 Introduction
    3.2 Rule-Based Information Extraction
    3.3 Named-Entity Recognition
        3.3.1 N-Gram Models
    3.4 Relation Extraction
    3.5 Evaluation
    3.6 Summary
    3.7 Exercises
        3.7.1 Regular Expressions
        3.7.2 Named-Entity Recognition
Chapter 4 Document Representation
    4.1 Introduction
    4.2 Document Indexing
    4.3 Vector Space Models
        4.3.1 Boolean Representation Model
        4.3.2 Term Frequency Model
        4.3.3 Inverse Document Frequency Model
    4.4 Summary
    4.5 Exercises
        4.5.1 TFxIDF Representation Model
Chapter 5 Association Rules Mining
    5.1 Introduction
    5.2 Association Patterns
    5.3 Evaluation
        5.3.1 Support
        5.3.2 Confidence
        5.3.3 Lift
    5.4 Association Rules Generation
    5.5 Summary
    5.6 Exercises
        5.6.1 Extraction of Association Rules
Chapter 6 Corpus-Based Semantic Analysis
    6.1 Introduction
    6.2 Corpus-Based Semantic Analysis
    6.3 Latent Semantic Analysis
        6.3.1 Creating Vectors with LSA
    6.4 Word2Vec
        6.4.1 Embedding Learning
        6.4.2 Prediction and Embeddings Interpretation
    6.5 Summary
    6.6 Exercises
        6.6.1 Latent Semantic Analysis
        6.6.2 Word Embedding with Word2Vec
Chapter 7 Document Clustering
    7.1 Introduction
    7.2 Document Clustering
    7.3 K-Means Clustering
    7.4 Self-Organizing Maps
        7.4.1 Topological Maps Learning
    7.5 Summary
    7.6 Exercises
        7.6.1 K-means Clustering
        7.6.2 Self-organizing Maps
Chapter 8 Topic Modeling
    8.1 Introduction
    8.2 Topic Modeling
    8.3 Latent Dirichlet Allocation
    8.4 Evaluation
    8.5 Summary
    8.6 Exercises
        8.6.1 Modeling Topics with LDA
Chapter 9 Document Categorization
    9.1 Introduction
    9.2 Categorization Models
    9.3 Bayesian Text Categorization
        9.3.1 Conditional Class Probability
        9.3.2 A Priori Probability
        9.3.3 Evidence
        9.3.4 Classification
    9.4 Maximum Entropy Categorization
    9.5 Evaluation
    9.6 Summary
    9.7 Exercises
        9.7.1 Naïve Bayes Categorization
        9.7.2 MaxEnt Categorization
Concluding Remarks
Bibliography
Glossary
Index