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

Description

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 Expressio ns 3.7.2 Named-Entity Recognit ion 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 Condfience 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 L SA 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 LD A 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