Natural Language Processing with Python --- Analyzing Text with the Natural Language Toolkit Steven Bird, Ewan Klein, and Edward Loper O'Reilly Media, 2009 | Sellers and prices

0. Preface (extras)

    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, Acknowledgments, About the Authors, Royalties,

1. Language Processing and Python (extras)

    1 Language Processing and Python, 1.1 Computing with Language: Texts and Words, Getting Started with Python, Getting Started with NLTK, Searching Text, Counting Vocabulary, 1.2 A Closer Look at Python: Texts as Lists of Words, Lists, Indexing Lists, Variables, Strings, 1.3 Computing with Language: Simple Statistics, Frequency Distributions, Fine-grained Selection of Words, Collocations and Bigrams, Counting Other Things, 1.4 Back to Python: Making Decisions and Taking Control, Conditionals, Operating on Every Element, Nested Code Blocks, Looping with Conditions, 1.5 Automatic Natural Language Understanding, Word Sense Disambiguation, Pronoun Resolution, Generating Language Output, Machine Translation, Spoken Dialog Systems, Textual Entailment, Limitations of NLP, 1.6 Summary, 1.7 Further Reading, 1.8 Exercises,

2. Accessing Text Corpora and Lexical Resources (extras)

    2 Accessing Text Corpora and Lexical Resources, 2.1 Accessing Text Corpora, Gutenberg Corpus, Web and Chat Text, Brown Corpus, Reuters Corpus, Inaugural Address Corpus, Annotated Text Corpora, Corpora in Other Languages, Text Corpus Structure, Loading your own Corpus, 2.2 Conditional Frequency Distributions, Conditions and Events, Counting Words by Genre, Plotting and Tabulating Distributions, Generating Random Text with Bigrams, 2.3 More Python: Reusing Code, Creating Programs with a Text Editor, Functions, Modules, 2.4 Lexical Resources, Wordlist Corpora, A Pronouncing Dictionary, Comparative Wordlists, Shoebox and Toolbox Lexicons, 2.5 WordNet, Senses and Synonyms, The WordNet Hierarchy, More Lexical Relations, Semantic Similarity, 2.6 Summary, 2.7 Further Reading, 2.8 Exercises,

3. Processing Raw Text

    3 Processing Raw Text, 3.1 Accessing Text from the Web and from Disk, Electronic Books, Dealing with HTML, Processing Search Engine Results, Processing RSS Feeds, Reading Local Files, Extracting Text from PDF, MSWord and other Binary Formats, Capturing User Input, The NLP Pipeline, Basic Operations with Strings, Printing Strings, Accessing Individual Characters, Accessing Substrings, More operations on strings, The Difference between Lists and Strings, 3.3 Text Processing with Unicode, What is Unicode?, Extracting encoded text from files, Using your local encoding in Python, Using Basic Meta-Characters, Ranges and Closures, Extracting Word Pieces, Doing More with Word Pieces, Finding Word Stems, Searching Tokenized Text, 3.6 Normalizing Text, Stemmers, Lemmatization, 3.7 Regular Expressions for Tokenizing Text, Simple Approaches to Tokenization, NLTK's Regular Expression Tokenizer, Further Issues with Tokenization, 3.8 Segmentation, Sentence Segmentation, Word Segmentation, From Lists to Strings, Strings and Formats, Lining Things Up, Writing Results to a File, Text Wrapping, 3.10 Summary, 3.11 Further Reading, 3.12 Exercises,

4. Writing Structured Programs (extras)

    4 Writing Structured Programs, 4.1 Back to the Basics, Assignment, Equality, Conditionals, 4.2 Sequences, Operating on Sequence Types, Combining Different Sequence Types, Generator Expressions, 4.3 Questions of Style, Python Coding Style, Procedural vs Declarative Style, Some Legitimate Uses for Counters, 4.4 Functions: The Foundation of Structured Programming, Function Inputs and Outputs, Parameter Passing, Variable Scope, Checking Parameter Types, Functional Decomposition, Documenting Functions, 4.5 Doing More with Functions, Functions as Arguments, Accumulative Functions, Higher-Order Functions, Named Arguments, 4.6 Program Development, Structure of a Python Module, Multi-Module Programs, Sources of Error, Debugging Techniques, Defensive Programming, 4.7 Algorithm Design, Recursion, Space-Time Tradeoffs, Dynamic Programming, 4.8 A Sample of Python Libraries, Matplotlib, NetworkX, csv, NumPy, Other Python Libraries, 4.9 Summary, 4.10 Further Reading, 4.11 Exercises,

5. Categorizing and Tagging Words

    5 Categorizing and Tagging Words, 5.1 Using a Tagger, 5.2 Tagged Corpora, Representing Tagged Tokens, Reading Tagged Corpora, A Simplified Part-of-Speech Tagset, Nouns, Verbs, Adjectives and Adverbs, Unsimplified Tags, Exploring Tagged Corpora, 5.3 Mapping Words to Properties Using Python Dictionaries, Indexing Lists vs Dictionaries, Dictionaries in Python, Defining Dictionaries, Default Dictionaries, Incrementally Updating a Dictionary, Complex Keys and Values, Inverting a Dictionary, 5.4 Automatic Tagging, The Default Tagger, The Regular Expression Tagger, The Lookup Tagger, Evaluation, 5.5 N-Gram Tagging, Unigram Tagging, Separating the Training and Testing Data, General N-Gram Tagging, Combining Taggers, Tagging Unknown Words, Storing Taggers, Performance Limitations, 5.6 Transformation-Based Tagging, 5.7 How to Determine the Category of a Word, Morphological Clues, Syntactic Clues, Semantic Clues, New Words, Morphology in Part of Speech Tagsets, 5.8 Summary, 5.9 Further Reading, 5.10 Exercises,

6. Learning to Classify Text (extras)

    6 Learning to Classify Text, 6.1 Supervised Classification, Gender Identification, Choosing The Right Features, Document Classification, Part-of-Speech Tagging, Exploiting Context, Sequence Classification, Other Methods for Sequence Classification, 6.2 Further Examples of Supervised Classification, Sentence Segmentation, Identifying Dialogue Act Types, Recognizing Textual Entailment, Scaling Up to Large Datasets, 6.3 Evaluation, The Test Set, Accuracy, Precision and Recall, Confusion Matrices, Cross-Validation, 6.4 Decision Trees, Entropy and Information Gain, 6.5 Naive Bayes Classifiers, Underlying Probabilistic Model, Zero Counts and Smoothing, Non-Binary Features, The Naivete of Independence, The Cause of Double-Counting, 6.6 Maximum Entropy Classifiers, The Maximum Entropy Model, Maximizing Entropy, Generative vs Conditional Classifiers, 6.7 Modeling Linguistic Patterns, What do models tell us?, 6.8 Summary, 6.9 Further Reading, 6.10 Exercises,

7. Extracting Information from Text

    7 Extracting Information from Text, 7.1 Information Extraction, Information Extraction Architecture, Noun Phrase Chunking, Tag Patterns, Chunking with Regular Expressions, Exploring Text Corpora, Chinking, Representing Chunks: Tags vs Trees, 7.3 Developing and Evaluating Chunkers, Reading IOB Format and the CoNLL 2000 Corpus, Simple Evaluation and Baselines, Training Classifier-Based Chunkers, 7.4 Recursion in Linguistic Structure, Building Nested Structure with Cascaded Chunkers, Trees, Tree Traversal, 7.5 Named Entity Recognition, 7.7 Summary, 7.8 Further Reading, 7.9 Exercises,

8. Analyzing Sentence Structure (extras)

    8 Analyzing Sentence Structure, 8.1 Some Grammatical Dilemmas, Linguistic Data and Unlimited Possibilities, Ubiquitous Ambiguity, 8.2 What's the Use of Syntax?, Beyond n-grams, 8.3 Context Free Grammar, A Simple Grammar, Writing Your Own Grammars, Recursion in Syntactic Structure, 8.4 Parsing With Context Free Grammar, Recursive Descent Parsing, Shift-Reduce Parsing, The Left-Corner Parser, Well-Formed Substring Tables, 8.5 Dependencies and Dependency Grammar, Valency and the Lexicon, Scaling Up, 8.6 Grammar Development, Treebanks and Grammars, Pernicious Ambiguity, Weighted Grammar, 8.7 Summary, 8.8 Further Reading, 8.9 Exercises,

9. Building Feature Based Grammars

    9 Building Feature Based Grammars, 9.1 Grammatical Features, Syntactic Agreement, Using Attributes and Constraints, Terminology, Subsumption and Unification, 9.3 Extending a Feature based Grammar, Subcategorization, Heads Revisited, Auxiliary Verbs and Inversion, Unbounded Dependency Constructions, Case and Gender in German, 9.4 Summary, 9.5 Further Reading, 9.6 Exercises,

10. Analyzing the Meaning of Sentences (extras)

    10 Analyzing the Meaning of Sentences, 10.1 Natural Language Understanding, Querying a Database, Natural Language, Semantics and Logic, 10.3 First-Order Logic, Syntax, First Order Theorem Proving, Summarizing the Language of First Order Logic, Truth in Model, Individual Variables and Assignments, Quantification, Quantifier Scope Ambiguity, Model Building, 10.4 The Semantics of English Sentences, Compositional Semantics in Feature-Based Grammar, The λ-Calculus, Quantified NPs, Transitive Verbs, Quantifier Ambiguity Revisited, 10.5 Discourse Semantics, Discourse Representation Theory, Discourse Processing, 10.6 Summary, 10.7 Further Reading, 10.8 Exercises,

11. Managing Linguistic Data

    11 Managing Linguistic Data, 11.1 Corpus Structure: a Case Study, The Structure of TIMIT, Notable Design Features, Fundamental Data Types, Three Corpus Creation Scenarios, Quality Control, Curation vs Evolution, 11.3 Acquiring Data, Obtaining Data from the Web, Obtaining Data from Word Processor Files, Obtaining Data from Spreadsheets and Databases, Converting Data Formats, Deciding Which Layers of Annotation to Include, Standards and Tools, Special Considerations when Working with Endangered Languages, 11.4 Working with XML, Using XML for Linguistic Structures, The Role of XML, The ElementTree Interface, Using ElementTree for Accessing Toolbox Data, Formatting Entries, 11.5 Working with Toolbox Data, Adding a Field to Each Entry, Validating a Toolbox Lexicon, 11.6 Describing Language Resources using OLAC Metadata, What is Metadata?, OLAC: Open Language Archives Community, Disseminating Language Resources, 11.7 Summary, 11.8 Further Reading, 11.9 Exercises,

12. Afterword: Facing the Language Challenge

    Afterword: The Language Challenge, Language Processing vs Symbol Processing, Contemporary Philosophical Divides, NLTK Roadmap, Envoi...,

Bibliography
Term Index

Errata (corrected here, and in the second printing of book (January 2010))

Translations: Book (jp), Prefácio (pt), Przedmowa (pl)

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