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