Natural Language Processing with Python - Helion
ebook
Autor: Steven Bird, Ewan Klein, Edward LoperISBN: 978-05-965-5571-9
stron: 504, Format: ebook
Data wydania: 2009-06-12
Księgarnia: Helion
Cena książki: 135,15 zł (poprzednio: 157,15 zł)
Oszczędzasz: 14% (-22,00 zł)
Tagi: Python - Programowanie
This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation. With it, you'll learn how to write Python programs that work with large collections of unstructured text. You'll access richly annotated datasets using a comprehensive range of linguistic data structures, and you'll understand the main algorithms for analyzing the content and structure of written communication.
Packed with examples and exercises, Natural Language Processing with Python will help you:
- Extract information from unstructured text, either to guess the topic or identify "named entities"
- Analyze linguistic structure in text, including parsing and semantic analysis
- Access popular linguistic databases, including WordNet and treebanks
- Integrate techniques drawn from fields as diverse as linguistics and artificial intelligence
This book will help you gain practical skills in natural language processing using the Python programming language and the Natural Language Toolkit (NLTK) open source library. If you're interested in developing web applications, analyzing multilingual news sources, or documenting endangered languages -- or if you're simply curious to have a programmer's perspective on how human language works -- you'll find Natural Language Processing with Python both fascinating and immensely useful.
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Spis treści
Natural Language Processing with Python eBook -- spis treści
- Natural Language Processing with Python
- SPECIAL OFFER: Upgrade this ebook with OReilly
- 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
- 1. Language Processing and Python
- Computing with Language: Texts and Words
- Getting Started with Python
- Getting Started with NLTK
- Searching Text
- Counting Vocabulary
- A Closer Look at Python: Texts as Lists of Words
- Lists
- Indexing Lists
- Variables
- Strings
- Computing with Language: Simple Statistics
- Frequency Distributions
- Fine-Grained Selection of Words
- Collocations and Bigrams
- Counting Other Things
- Back to Python: Making Decisions and Taking Control
- Conditionals
- Operating on Every Element
- Nested Code Blocks
- Looping with Conditions
- Automatic Natural Language Understanding
- Word Sense Disambiguation
- Pronoun Resolution
- Generating Language Output
- Machine Translation
- Spoken Dialogue Systems
- Textual Entailment
- Limitations of NLP
- Summary
- Further Reading
- Exercises
- Computing with Language: Texts and Words
- 2. Accessing Text Corpora and Lexical Resources
- 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
- Conditional Frequency Distributions
- Conditions and Events
- Counting Words by Genre
- Plotting and Tabulating Distributions
- Generating Random Text with Bigrams
- More Python: Reusing Code
- Creating Programs with a Text Editor
- Functions
- Modules
- Lexical Resources
- Wordlist Corpora
- A Pronouncing Dictionary
- Comparative Wordlists
- Shoebox and Toolbox Lexicons
- WordNet
- Senses and Synonyms
- The WordNet Hierarchy
- More Lexical Relations
- Semantic Similarity
- Summary
- Further Reading
- Exercises
- Accessing Text Corpora
- 3. Processing Raw Text
- 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
- Strings: Text Processing at the Lowest Level
- Basic Operations with Strings
- Printing Strings
- Accessing Individual Characters
- Accessing Substrings
- More Operations on Strings
- The Difference Between Lists and Strings
- Text Processing with Unicode
- What Is Unicode?
- Extracting Encoded Text from Files
- Using Your Local Encoding in Python
- Regular Expressions for Detecting Word Patterns
- Using Basic Metacharacters
- Ranges and Closures
- Useful Applications of Regular Expressions
- Extracting Word Pieces
- Doing More with Word Pieces
- Finding Word Stems
- Searching Tokenized Text
- Normalizing Text
- Stemmers
- Lemmatization
- Regular Expressions for Tokenizing Text
- Simple Approaches to Tokenization
- NLTKs Regular Expression Tokenizer
- Further Issues with Tokenization
- Segmentation
- Sentence Segmentation
- Word Segmentation
- Formatting: From Lists to Strings
- From Lists to Strings
- Strings and Formats
- Lining Things Up
- Writing Results to a File
- Text Wrapping
- Summary
- Further Reading
- Exercises
- Accessing Text from the Web and from Disk
- 4. Writing Structured Programs
- Back to the Basics
- Assignment
- Equality
- Conditionals
- Sequences
- Operating on Sequence Types
- Combining Different Sequence Types
- Generator Expressions
- Questions of Style
- Python Coding Style
- Procedural Versus Declarative Style
- Some Legitimate Uses for Counters
- Functions: The Foundation of Structured Programming
- Function Inputs and Outputs
- Parameter Passing
- Variable Scope
- Checking Parameter Types
- Functional Decomposition
- Documenting Functions
- Doing More with Functions
- Functions As Arguments
- Accumulative Functions
- Higher-Order Functions
- Named Arguments
- Program Development
- Structure of a Python Module
- Multimodule Programs
- Sources of Error
- Debugging Techniques
- Defensive Programming
- Algorithm Design
- Recursion
- Space-Time Trade-offs
- Dynamic Programming
- A Sample of Python Libraries
- Matplotlib
- NetworkX
- csv
- NumPy
- Other Python Libraries
- Summary
- Further Reading
- Exercises
- Back to the Basics
- 5. Categorizing and Tagging Words
- Using a Tagger
- Tagged Corpora
- Representing Tagged Tokens
- Reading Tagged Corpora
- A Simplified Part-of-Speech Tagset
- Nouns
- Verbs
- Adjectives and Adverbs
- Unsimplified Tags
- Exploring Tagged Corpora
- Mapping Words to Properties Using Python Dictionaries
- Indexing Lists Versus Dictionaries
- Dictionaries in Python
- Defining Dictionaries
- Default Dictionaries
- Incrementally Updating a Dictionary
- Complex Keys and Values
- Inverting a Dictionary
- Automatic Tagging
- The Default Tagger
- The Regular Expression Tagger
- The Lookup Tagger
- Evaluation
- N-Gram Tagging
- Unigram Tagging
- Separating the Training and Testing Data
- General N-Gram Tagging
- Combining Taggers
- Tagging Unknown Words
- Storing Taggers
- Performance Limitations
- Tagging Across Sentence Boundaries
- Transformation-Based Tagging
- How to Determine the Category of a Word
- Morphological Clues
- Syntactic Clues
- Semantic Clues
- New Words
- Morphology in Part-of-Speech Tagsets
- Summary
- Further Reading
- Exercises
- 6. Learning to Classify Text
- Supervised Classification
- Gender Identification
- Choosing the Right Features
- Document Classification
- Part-of-Speech Tagging
- Exploiting Context
- Sequence Classification
- Other Methods for Sequence Classification
- Further Examples of Supervised Classification
- Sentence Segmentation
- Identifying Dialogue Act Types
- Recognizing Textual Entailment
- Scaling Up to Large Datasets
- Evaluation
- The Test Set
- Accuracy
- Precision and Recall
- Confusion Matrices
- Cross-Validation
- Decision Trees
- Entropy and Information Gain
- Naive Bayes Classifiers
- Underlying Probabilistic Model
- Zero Counts and Smoothing
- Non-Binary Features
- The Naivete of Independence
- The Cause of Double-Counting
- Maximum Entropy Classifiers
- The Maximum Entropy Model
- Maximizing Entropy
- Generative Versus Conditional Classifiers
- Modeling Linguistic Patterns
- What Do Models Tell Us?
- Summary
- Further Reading
- Exercises
- Supervised Classification
- 7. Extracting Information from Text
- Information Extraction
- Information Extraction Architecture
- Chunking
- Noun Phrase Chunking
- Tag Patterns
- Chunking with Regular Expressions
- Exploring Text Corpora
- Chinking
- Representing Chunks: Tags Versus Trees
- Developing and Evaluating Chunkers
- Reading IOB Format and the CoNLL-2000 Chunking Corpus
- Simple Evaluation and Baselines
- Training Classifier-Based Chunkers
- Recursion in Linguistic Structure
- Building Nested Structure with Cascaded Chunkers
- Trees
- Tree Traversal
- Named Entity Recognition
- Relation Extraction
- Summary
- Further Reading
- Exercises
- Information Extraction
- 8. Analyzing Sentence Structure
- Some Grammatical Dilemmas
- Linguistic Data and Unlimited Possibilities
- Ubiquitous Ambiguity
- Whats the Use of Syntax?
- Beyond n-grams
- Context-Free Grammar
- A Simple Grammar
- Writing Your Own Grammars
- Recursion in Syntactic Structure
- Parsing with Context-Free Grammar
- Recursive Descent Parsing
- Shift-Reduce Parsing
- The Left-Corner Parser
- Well-Formed Substring Tables
- Dependencies and Dependency Grammar
- Valency and the Lexicon
- Scaling Up
- Grammar Development
- Treebanks and Grammars
- Pernicious Ambiguity
- Weighted Grammar
- Summary
- Further Reading
- Exercises
- Some Grammatical Dilemmas
- 9. Building Feature-Based Grammars
- Grammatical Features
- Syntactic Agreement
- Using Attributes and Constraints
- Terminology
- Processing Feature Structures
- Subsumption and Unification
- Extending a Feature-Based Grammar
- Subcategorization
- Heads Revisited
- Auxiliary Verbs and Inversion
- Unbounded Dependency Constructions
- Case and Gender in German
- Summary
- Further Reading
- Exercises
- Grammatical Features
- 10. Analyzing the Meaning of Sentences
- Natural Language Understanding
- Querying a Database
- Natural Language, Semantics, and Logic
- Propositional Logic
- 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
- The Semantics of English Sentences
- Compositional Semantics in Feature-Based Grammar
- The -Calculus
- Quantified NPs
- Transitive Verbs
- Quantifier Ambiguity Revisited
- Discourse Semantics
- Discourse Representation Theory
- Discourse Processing
- Summary
- Further Reading
- Exercises
- Natural Language Understanding
- 11. Managing Linguistic Data
- Corpus Structure: A Case Study
- The Structure of TIMIT
- Notable Design Features
- Fundamental Data Types
- The Life Cycle of a Corpus
- Three Corpus Creation Scenarios
- Quality Control
- Curation Versus Evolution
- 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
- Working with XML
- Using XML for Linguistic Structures
- The Role of XML
- The ElementTree Interface
- Using ElementTree for Accessing Toolbox Data
- Formatting Entries
- Working with Toolbox Data
- Adding a Field to Each Entry
- Validating a Toolbox Lexicon
- Describing Language Resources Using OLAC Metadata
- What Is Metadata?
- OLAC: Open Language Archives Community
- Summary
- Further Reading
- Exercises
- Corpus Structure: A Case Study
- A. Afterword: The Language Challenge
- Language Processing Versus Symbol Processing
- Contemporary Philosophical Divides
- NLTK Roadmap
- Envoi...
- B. Bibliography
- NLTK Index
- General Index
- About the Authors
- Colophon
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