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Python 3 Text Processing with NLTK 3 Cookbook

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

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Product type Paperback
Published in Aug 2014
Publisher
ISBN-13 9781782167853
Length 304 pages
Edition 2nd Edition
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Author (1):
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Jacob Perkins Jacob Perkins
Author Profile Icon Jacob Perkins
Jacob Perkins
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Table of Contents (12) Chapters Close

Preface 1. Tokenizing Text and WordNet Basics FREE CHAPTER 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 A. Penn Treebank Part-of-speech Tags
Index

Storing a frequency distribution in Redis


The nltk.probability.FreqDist class is used in many classes throughout NLTK for storing and managing frequency distributions. It's quite useful, but it's all in-memory, and doesn't provide a way to persist the data. A single FreqDist is also not accessible to multiple processes. We can change all that by building a FreqDist on top of Redis.

Redis is a data structure server that is one of the more popular NoSQL databases. Among other things, it provides a network-accessible database for storing dictionaries (also known as hash maps). Building a FreqDist interface to a Redis hash map will allow us to create a persistent FreqDist that is accessible to multiple local and remote processes at the same time.

Note

Most Redis operations are atomic, so it's even possible to have multiple processes write to the FreqDist concurrently.

Getting ready

For this and the subsequent recipes, we need to install both Redis and redis-py. The Redis website is at http://redis...

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