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Python text processing with NLTK 2.0 Cookbook : over 80 practical recipes for using Python's NLTK suite of libraries to maximize your natural language processing capabilities

Author: Jacob Perkins
Publisher: Birmingham ; Mumbai : PACKT Publishing, 2010.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Database:WorldCat
Summary:
The learn-by-doing approach of this book will enable you to dive right into the heart of text processing from the very first page. Each recipe is carefully designed to fulfill your appetite for Natural Language Processing. Packed with numerous illustrative examples and code samples, it will make the task of using the NLTK for Natural Language Processing easy and straightforward. This book is for Python programmers  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Perkins, Jacob.
Python text processing with NTLK 2.0 Cookbook.
Birmingham ; Mumbai : PACKT Publishing, 2010
(OCoLC)711962863
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Jacob Perkins
ISBN: 9781849513616 1849513619
OCLC Number: 775351847
Notes: "Open source community experience distilled."
"Quick answers to common problems"--Cover.
Includes index.
Description: 1 online resource (iii, 256 pages) : illustrations
Contents: Cover --
Copyright --
Credits --
About the Author --
About the Reviewers --
Table of Contents --
Preface --
Chapter 1: Tokenizing Text and Wordnet Basics --
Introduction --
Tokenizing Text Into Sentences --
Tokenizing Sentences Into Words --
Tokenizing Sentences Using Regular --
Expressions --
Filtering Stopwords in a Tokenized Sentence --
Looking Up Synsets for a Word in Wordnet --
Looking Up Lemmas and Synonyms --
in Wordnet --
Calculating Wordnet Synset Similarity --
Discovering Word Collocations --
Chapter 2: Replacing and Correcting Words --
Introduction --
Stemming Words --
Lemmatizing Words With Wordnet --
Translating Text With Babelfish --
Replacing Words Matching Regular --
Removing Repeating Characters --
Spelling Correction With Enchant --
Replacing Synonyms --
Replacing Negations With Antonyms --
Chapter 3: Creating Custom Corpora --
Introduction --
Setting Up a Custom Corpus --
Creating a Word List Corpus --
Creating a Part-of-Speech Tagged Word --
Corpus --
Creating a Chunked Phrase Corpus --
Creating a Categorized Text Corpus --
Creating a Categorized Chunk Corpus Reader --
Lazy Corpus Loading --
Creating a Custom Corpus View --
Creating a Mongodb Backed Corpus Reader --
Corpus Editing With File Locking --
Chapter 4: Part-of-Speech Tagging --
Introduction --
Default Tagging --
Training a Unigram Part-of-Speech Tagger --
Combining Taggers With Backoff Tagging --
Training and Combining Ngram Taggers --
Creating a Model of Likely Word Tags --
Tagging With Regular Expressions --
Affix Tagging --
Training a Brill Tagger --
Training the Tnt Tagger --
Using Wordnet for Tagging --
Tagging Proper Names --
Classifier Based Tagging --
Chapter 5: Extracting Chunks --
Introduction --
Chunking and Chinking With Regular --
Merging and Splitting Chunks With Regular Expressions --
Expanding and Removing Chunks With --
Regular Expressions --
Partial Parsing With Regular Expressions --
Training a Tagger-Based Chunker --
Classification-Based Chunking --
Extracting Named Entities --
Extracting Proper Noun Chunks --
Extracting Location Chunks --
Training a Named Entity Chunker --
Chapter 6: Transforming Chunks and Trees --
Introduction --
Filtering Insignificant Words --
Correcting Verb Forms --
Swapping Verb Phrases --
Swapping Noun Cardinals --
Swapping Infinitive Phrases --
Singularizing Plural Nouns --
Chaining Chunk Transformations --
Converting a Chunk Tree to Text --
Flattening a Deep Tree --
Creating a Shallow Tree --
Converting Tree Nodes --
Chapter 7: Text Classification --
Introduction --
Bag of Words Feature Extraction --
Training a Naive Bayes Classifier --
Training a Decision Tree Classifier --
Training a Maximum Entropy Classifier --
Measuring Precision and Recall of a --
Classifier --
Calculating High Information Words --
Combining Classifiers With Voting --
Classifying With Multiple Binary Classifiers --
Chapte.
Other Titles: Over 80 practical recipes for using Python's NLTK suite of libraries to maximize your natural language processing capabilities
Over 80 practical recipes for using Python's Natural Language Toolkit suite of libraries to maximize your natural language processing capabilities
Responsibility: Jacob Perkins.

Abstract:

The learn-by-doing approach of this book will enable you to dive right into the heart of text processing from the very first page. Each recipe is carefully designed to fulfill your appetite for Natural Language Processing. Packed with numerous illustrative examples and code samples, it will make the task of using the NLTK for Natural Language Processing easy and straightforward. This book is for Python programmers who want to quickly get to grips with using the NLTK for Natural Language Processing. Familiarity with basic text processing concepts is required. Programmers experienced in the NLTK will also find it useful. Students of linguistics will find it invaluable.

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