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Python Natural Language Processing Cookbook

You're reading from   Python Natural Language Processing Cookbook Over 50 recipes to understand, analyze, and generate text for implementing language processing tasks

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Product type Paperback
Published in Mar 2021
Publisher Packt
ISBN-13 9781838987312
Length 284 pages
Edition 1st Edition
Languages
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Author (1):
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Zhenya Antić Zhenya Antić
Author Profile Icon Zhenya Antić
Zhenya Antić
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Toc

Table of Contents (10) Chapters Close

Preface 1. Chapter 1: Learning NLP Basics 2. Chapter 2: Playing with Grammar FREE CHAPTER 3. Chapter 3: Representing Text – Capturing Semantics 4. Chapter 4: Classifying Texts 5. Chapter 5: Getting Started with Information Extraction 6. Chapter 6: Topic Modeling 7. Chapter 7: Building Chatbots 8. Chapter 8: Visualizing Text Data 9. Other Books You May Enjoy

Dividing sentences into words – tokenization

In many instances, we rely on individual words when we do NLP tasks. This happens, for example, when we build semantic models of texts by relying on the semantics of individual words, or when we are looking for words with a specific part of speech. To divide text into words, we can use NLTK and spaCy.

Getting ready

For this recipe, we will be using the same text of the book The Adventures of Sherlock Holmes. You can find the whole text in the book's GitHub repository. For this recipe, we will need just the beginning of the book, which can be found in the sherlock_holmes_1.txt file.

In order to do this task, you will need the nltk package, described in the Technical requirements section.

How to do it…

  1. Import the nltk package:
    import nltk
  2. Read in the book text:
    filename = "sherlock_holmes_1.txt"
    file = open(filename, "r", encoding="utf-8")
    text = file.read()
  3. Replace...
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