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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

Stemming words


Stemming is a technique to remove affixes from a word, ending up with the stem. For example, the stem of cooking is cook, and a good stemming algorithm knows that the ing suffix can be removed. Stemming is most commonly used by search engines for indexing words. Instead of storing all forms of a word, a search engine can store only the stems, greatly reducing the size of index while increasing retrieval accuracy.

One of the most common stemming algorithms is the Porter stemming algorithm by Martin Porter. It is designed to remove and replace well-known suffixes of English words, and its usage in NLTK will be covered in the next section.

Note

The resulting stem is not always a valid word. For example, the stem of cookery is cookeri. This is a feature, not a bug.

How to do it...

NLTK comes with an implementation of the Porter stemming algorithm, which is very easy to use. Simply instantiate the PorterStemmer class and call the stem() method with the word you want to stem:

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