Natural Language Processing with Python

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;