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Python 3 Text Processing with NLTK 3 Cookbook

You're reading from  Python 3 Text Processing with NLTK 3 Cookbook

Product type Book
Published in Aug 2014
Publisher
ISBN-13 9781782167853
Pages 304 pages
Edition 1st Edition
Languages
Author (1):
Jacob Perkins Jacob Perkins
Profile icon Jacob Perkins

Table of Contents (17) Chapters

Python 3 Text Processing with NLTK 3 Cookbook
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. Tokenizing Text and WordNet Basics 2. Replacing and Correcting Words 3. Creating Custom Corpora 4. Part-of-speech Tagging 5. Extracting Chunks 6. Transforming Chunks and Trees 7. Text Classification 8. Distributed Processing and Handling Large Datasets 9. Parsing Specific Data Types Penn Treebank Part-of-speech Tags
Index

Lazy corpus loading


Loading a corpus reader can be an expensive operation due to the number of files, file sizes, and various initialization tasks. And while you'll often want to specify a corpus reader in a common module, you don't always need to access it right away. To speed up module import time when a corpus reader is defined, NLTK provides a LazyCorpusLoader class that can transform itself into your actual corpus reader as soon as you need it. This way, you can define a corpus reader in a common module without it slowing down module loading.

How to do it...

The LazyCorpusLoader class requires two arguments: the name of the corpus and the corpus reader class, plus any other arguments needed to initialize the corpus reader class.

The name argument specifies the root directory name of the corpus, which must be within a corpora subdirectory of one of the paths in nltk.data.path. See the Setting up a custom corpus recipe of this chapter for more details on nltk.data.path.

For example, if you...

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