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Natural Language Processing with Python : Analyzing Text with the Natural Language Toolkit by Ewan Klein, Steven Bird and Edward Loper (2009, Trade Paperback)

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Product Information

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.

Product Identifiers

Publisher
O'reilly Media, Incorporated
ISBN-10
0596516495
ISBN-13
9780596516499
eBay Product ID (ePID)
71895926

Product Key Features

Author
Ewan Klein, Edward Loper, Steven Bird
Publication Name
Natural Language Processing with Python : Analyzing Text with The Natural Language Toolkit
Format
Trade Paperback
Language
English
Publication Year
2009
Type
Textbook
Number of Pages
502 Pages

Dimensions

Item Length
9.1in
Item Height
1.2in
Item Width
7in
Item Weight
31.3 Oz

Additional Product Features

Lc Classification Number
Qa76.73.P98
Table of Content
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;
Copyright Date
2009
Target Audience
Scholarly & Professional
Topic
Programming Languages / General, Natural Language Processing, General, Programming Languages / Python
Illustrated
Yes
Genre
Computers

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Most relevant reviews

5 out of 5 stars
by ssytE0GlQbu@Del...Feb 04, 2011
Great For All Levels
I bought this book because a teacher recommended it for a project. I had used very little Python before, and just by working through it I feel very confident in the programming language, as well as using NLTK. It assumes you have little programming experiance, and works as far. For those who are more experianced with programming languages you can actually skip some sections. Great book for anyone who wants to learn to use NLTK as well as learn Python
4 out of 5 stars
by oskar700Sep 18, 2021
Great book on NLP.
Very good book on NLP. Very easy to digest. Examples great.
Verified purchase: Yes | Condition: Pre-owned

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