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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 conditional frequency distribution in Redis


The nltk.probability.ConditionalFreqDist class is a container for FreqDist instances, with one FreqDist per condition. It is used to count frequencies that are dependent on another condition, such as another word or a class label. We used this class in the Calculating high information words recipe in Chapter 7, Text Classification. Here, we'll create an API-compatible class on top of Redis using the RedisHashFreqDist from the previous recipe.

Getting ready

As in the previous recipe, you'll need to have Redis and redis-py installed with an instance of redis-server running.

How to do it...

We define a RedisConditionalHashFreqDist class in redisprob.py that extends nltk.probability.ConditionalFreqDist and overrides the __getitem__() method. We override __getitem__() so we can create an instance of RedisHashFreqDist instead of a FreqDist:

from nltk.probability import ConditionalFreqDist
from rediscollections import encode_key

class RedisConditionalHashFreqDist...
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