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Preface; Audience; Emphasis; What You Will Learn; Organization; Why Python?; Software Requirements; Natural Language Toolkit (NLTK); For Instructors; Conventions Used in This Book; Using Code Examples; Safari® Books Online; How to Contact Us; Acknowledgments; Royalties;
Chapter 1: Language Processing and Python;
1.1 Computing with Language: Texts and Words;
1.2 A Closer Look at Python: Texts as Lists of Words;
1.3 Computing with Language: Simple Statistics;
1.4 Back to Python: Making Decisions and Taking Control;
1.5 Automatic Natural Language Understanding;
1.6 Summary;
1.7 Further Reading;
1.8 Exercises;
Chapter 2: Accessing Text Corpora and Lexical Resources;
2.1 Accessing Text Corpora;
2.2 Conditional Frequency Distributions;
2.3 More Python: Reusing Code;
2.4 Lexical Resources;
2.5 WordNet;
2.6 Summary;
2.7 Further Reading;
2.8 Exercises;
Chapter 3: Processing Raw Text;
3.1 Accessing Text from the Web and from Disk;
3.2 Strings: Text Processing at the Lowest Level;
3.3 Text Processing with Unicode;
3.4 Regular Expressions for Detecting Word Patterns;
3.5 Useful Applications of Regular Expressions;
3.6 Normalizing Text;
3.7 Regular Expressions for Tokenizing Text;
3.8 Segmentation;
3.9 Formatting: From Lists to Strings;
3.10 Summary;
3.11 Further Reading;
3.12 Exercises;
Chapter 4: Writing Structured Programs;
4.1 Back to the Basics;
4.2 Sequences;
4.3 Questions of Style;
4.4 Functions: The Foundation of Structured Programming;
4.5 Doing More with Functions;
4.6 Program Development;
4.7 Algorithm Design;
4.8 A Sample of Python Libraries;
4.9 Summary;
4.10 Further Reading;
4.11 Exercises;
Chapter 5: Categorizing and Tagging Words;
5.1 Using a Tagger;
5.2 Tagged Corpora;
5.3 Mapping Words to Properties Using Python Dictionaries;
5.4 Automatic Tagging;
5.5 N-Gram Tagging;
5.6 Transformation-Based Tagging;
5.7 How to Determine the Category of a Word;
5.8 Summary;
5.9 Further Reading;
5.10 Exercises;
Chapter 6: Learning to Classify Text;
6.1 Supervised Classification;
6.2 Further Examples of Supervised Classification;
6.3 Evaluation;
6.4 Decision Trees;
6.5 Naive Bayes Classifiers;
6.6 Maximum Entropy Classifiers;
6.7 Modeling Linguistic Patterns;
6.8 Summary;
6.9 Further Reading;
6.10 Exercises;
Chapter 7: Extracting Information from Text;
7.1 Information Extraction;
7.2 Chunking;
7.3 Developing and Evaluating Chunkers;
7.4 Recursion in Linguistic Structure;
7.5 Named Entity Recognition;
7.6 Relation Extraction;
7.7 Summary;
7.8 Further Reading;
7.9 Exercises;
Chapter 8: Analyzing Sentence Structure;
8.1 Some Grammatical Dilemmas;
8.2 What's the Use of Syntax?;
8.3 Context-Free Grammar;
8.4 Parsing with Context-Free Grammar;
8.5 Dependencies and Dependency Grammar;
8.6 Grammar Development;
8.7 Summary;
8.8 Further Reading;
8.9 Exercises;
Chapter 9: Building Feature-Based Grammars;
9.1 Grammatical Features;
9.2 Processing Feature Structures;
9.3 Extending a Feature-Based Grammar;
9.4 Summary;
9.5 Further Reading;
9.6 Exercises;
Chapter 10: Analyzing the Meaning of Sentences;
10.1 Natural Language Understanding;
10.2 Propositional Logic;
10.3 First-Order Logic;
10.4 The Semantics of English Sentences;
10.5 Discourse Semantics;
10.6 Summary;
10.7 Further Reading;
10.8 Exercises;
Chapter 11: Managing Linguistic Data;
11.1 Corpus Structure: A Case Study;
11.2 The Life Cycle of a Corpus;
11.3 Acquiring Data;
11.4 Working with XML;
11.5 Working with Toolbox Data;
11.6 Describing Language Resources Using OLAC Metadata;
11.7 Summary;
11.8 Further Reading;
11.9 Exercises;Afterword: The Language Challenge; Language Processing Versus Symbol Processing; Contemporary Philosophical Divides; NLTK Roadmap; Envoi...;Bibliography;NLTK Index;General Index;Colophon;