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Natural Language Processing: Python and NLTK

You're reading from   Natural Language Processing: Python and NLTK Learn to build expert NLP and machine learning projects using NLTK and other Python libraries

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Product type Course
Published in Nov 2016
Publisher Packt
ISBN-13 9781787285101
Length 702 pages
Edition 1st Edition
Languages
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Authors (5):
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Iti Mathur Iti Mathur
Author Profile Icon Iti Mathur
Iti Mathur
Jacob Perkins Jacob Perkins
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Jacob Perkins
Deepti Chopra Deepti Chopra
Author Profile Icon Deepti Chopra
Deepti Chopra
Nitin Hardeniya Nitin Hardeniya
Author Profile Icon Nitin Hardeniya
Nitin Hardeniya
Nisheeth Joshi Nisheeth Joshi
Author Profile Icon Nisheeth Joshi
Nisheeth Joshi
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Chapter 2. Replacing and Correcting Words

In this chapter, we will cover the following recipes:

  • Stemming words
  • Lemmatizing words with WordNet
  • Replacing words matching regular expressions
  • Removing repeating characters
  • Spelling correction with Enchant
  • Replacing synonyms
  • Replacing negations with antonyms

Introduction

In this chapter, we will go over various word replacement and correction techniques. The recipes cover the gamut of linguistic compression, spelling correction, and text normalization. All of these methods can be very useful for preprocessing text before search indexing, document classification, and text analysis.

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...

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Natural Language Processing: Python and NLTK
Published in: Nov 2016
Publisher: Packt
ISBN-13: 9781787285101
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