Steven Bird

Natural Language Processing with Python (eBook, PDF)

• Format: PDF

Produktbeschreibung

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 "e;named entities"e;Analyze linguistic structure in text, including parsing and semantic analysisAccess popular linguistic databases, including WordNet and treebanksIntegrate techniques drawn from fields as diverse as linguistics and artificial intelligenceThis 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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Produktdetails

• Verlag: O'Reilly Media
• Seitenzahl: 504
• Erscheinungstermin: 12. Juni 2009
• Englisch
• ISBN-13: 9780596550967
• Artikelnr.: 53003541

Autorenporträt

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. Ewan Klein is Professor of Language Technology in the School of Informatics at the University of Edinburgh. He completed a PhD on formal semantics at the University of Cambridge in 1978. After some years working at the Universities of Sussex and Newcastle upon Tyne, Ewan took up a teaching position at Edinburgh. He was involved in the establishment of Edinburgh's Language Technology Group in 1993, and has been closely associated with it ever since. From 2000-2002, he took leave from the University to act as Research Manager for the Edinburgh-based Natural Language Research Group of Edify Corporation, Santa Clara, and was responsible for spoken dialogue processing. Ewan is a past President of the European Chapter of the Association for Computational Linguistics and was a founding member and Coordinator of the European Network of Excellence in Human Language Technologies (ELSNET). Edward Loper has recently completed a PhD on machine learning for natural language processing at the the University of Pennsylvania. Edward was a student in Steven's graduate course on computational linguistics in the fall of 2000, and went on to be a TA and share in the development of NLTK. In addition to NLTK, he has helped develop two packages for documenting and testing Python software, epydoc, and doctest.

Inhaltsangabe

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
Summary
Further Reading
Exercises
Chapter 2 Accessing Text Corpora and Lexical
Resources
Accessing Text Corpora
Conditional Frequency Distributions
More Python: Reusing Code
Lexical Resources
WordNet
Summary
Further Reading
Exercises
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
Segmentation
Formatting: From Lists to Strings
Summary
Further Reading
Exercises
Chapter 4 Writing Structured Programs
Back to the Basics
Sequences
Questions of Style
Functions: The Foundation of Structured Programming
Doing More with Functions
Program Development
Algorithm Design
A Sample of Python Libraries
Summary
Further Reading
Exercises
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
Summary
Further Reading
Exercises
Chapter 6 Learning to Classify Text
Supervised Classification
Further Examples of Supervised Classification
Evaluation
Decision Trees
Naive Bayes Classifiers
Maximum Entropy Classifiers
Modeling Linguistic Patterns
Summary
Further Reading
Exercises
Chapter 7 Extracting Information from Text
Information Extraction Chunking
Developing and Evaluating Chunkers
Recursion in Linguistic Structure
Named Entity Recognition
Relation Extraction
Summary
Further Reading
Exercises
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
Summary
Further Reading
Exercises
Chapter 9 Building Feature-Based Grammars
Grammatical Features
Processing Feature Structures
Extending a Feature-Based Grammar
Summary
Further Reading
Exercises
Chapter 10 Analyzing the Meaning of Sentences
Natural Language Understanding
Propositional Logic
First-Order Logic
The Semantics of English Sentences
Discourse Semantics
Summary
Further Reading
Exercises
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
Summary
Further Reading
Exercises
Appendix Afterword: The Language Challenge
Language Processing Versus Symbol Processing
Contemporary Philosophical Divides
NLTK Roadmap
Envoi...
Appendix Bibliography
NLTK Index
General Index
Colophon

Rezensionen

"Natural Language Processing with Python ist ein gelungenes Einsteigerwerk für die computergestützte Textanalyse. Konkrete Code-Beispiele werden von theoretischen Erläuterungen begleitet. Das Buch motiviert zu eigenen Experimenten und sorgt so schnell für Erfolgserlebnisse. Es bietet pragmatisches Grundlagenwissen und wirbt für eine weitergehende Beschäftigung mit dem Thema." -- IT-Rezensionen.de, Januar 2010