Natural Language Processing with Python

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.
Steven Bird is Associate Professor in the Department of Computer Science and Software Engineering at the University of Melbourne, and Senior Research Associate in the Linguistic Data Consortium at the University of Pennsylvania. He completed a PhD on computational phonology at the University of Edinburgh in 1990, supervised by Ewan Klein. He later moved to Cameroon to conduct linguistic fieldwork on the Grassfields Bantu languages under the auspices of the Summer Institute of Linguistics. More recently, he spent several years as Associate Director of the Linguistic Data Consortium where he led an R&D team to create models and tools for large databases of annotated text. At Melbourne University, he established a language technology research group and has taught at all levels of the undergraduate computer science curriculum. In 2009, Steven is President of the Association for Computational Linguistics.
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  • Inhaltsverzeichnis
    Chapter 1 Language Processing and Python
    Computing with Language: Texts and Words
    A Closer Look at Python: Texts as Lists of Words
    Computing with Language: Simple Statistics
    Back to Python: Making Decisions and Taking Control
    Automatic Natural Language Understanding
    Further Reading
    Chapter 2 Accessing Text Corpora and Lexical
    Accessing Text Corpora
    Conditional Frequency Distributions
    More Python: Reusing Code
    Lexical Resources
    Further Reading
    Chapter 3 Processing Raw Text
    Accessing Text from the Web and from Disk
    Strings: Text Processing at the Lowest Level
    Text Processing with Unicode
    Regular Expressions for Detecting Word Patterns
    Useful Applications of Regular Expressions
    Normalizing Text
    Regular Expressions for Tokenizing Text
    Formatting: From Lists to Strings
    Further Reading
    Chapter 4 Writing Structured Programs
    Back to the Basics
    Questions of Style
    Functions: The Foundation of Structured Programming
    Doing More with Functions
    Program Development
    Algorithm Design
    A Sample of Python Libraries
    Further Reading
    Chapter 5 Categorizing and Tagging Words
    Using a Tagger
    Tagged Corpora
    Mapping Words to Properties Using Python Dictionaries
    Automatic Tagging
    N-Gram Tagging
    Transformation-Based Tagging
    How to Determine the Category of a Word
    Further Reading
    Chapter 6 Learning to Classify Text
    Supervised Classification
    Further Examples of Supervised Classification
    Decision Trees
    Naive Bayes Classifiers
    Maximum Entropy Classifiers
    Modeling Linguistic Patterns
    Further Reading
    Chapter 7 Extracting Information from Text
    Information Extraction Chunking
    Developing and Evaluating Chunkers
    Recursion in Linguistic Structure
    Named Entity Recognition
    Relation Extraction
    Further Reading
    Chapter 8 Analyzing Sentence Structure
    Some Grammatical Dilemmas
    What s the Use of Syntax?
    Context-Free Grammar
    Parsing with Context-Free Grammar
    Dependencies and Dependency Grammar
    Grammar Development
    Further Reading
    Chapter 9 Building Feature-Based Grammars
    Grammatical Features
    Processing Feature Structures
    Extending a Feature-Based Grammar
    Further Reading
    Chapter 10 Analyzing the Meaning of Sentences
    Natural Language Understanding
    Propositional Logic
    First-Order Logic
    The Semantics of English Sentences
    Discourse Semantics
    Further Reading
    Chapter 11 Managing Linguistic Data
    Corpus Structure: A Case Study
    The Life Cycle of a Corpus
    Acquiring Data
    Working with XML
    Working with Toolbox Data
    Describing Language Resources Using OLAC Metadata
    Further Reading
    Appendix Afterword: The Language Challenge
    Language Processing Versus Symbol Processing
    Contemporary Philosophical Divides
    NLTK Roadmap
    Appendix Bibliography
    NLTK Index
    General Index
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Einband Taschenbuch
Herausgeber Julie Steele
Seitenzahl 479
Erscheinungsdatum 01.07.2009
Sprache Englisch
ISBN 978-0-596-51649-9
Verlag O'Reilly UK Ltd.
Maße (L/B/H) 23.8/17.9/3.2 cm
Gewicht 810 g
Illustrator Robert Romano
Verkaufsrang 24851
Buch (Taschenbuch, Englisch)
Buch (Taschenbuch, Englisch)
Fr. 48.90
Fr. 48.90
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