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Python Natural Language Processing Cookbook

You're reading from   Python Natural Language Processing Cookbook Over 60 recipes for building powerful NLP solutions using Python and LLM libraries

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Product type Paperback
Published in Sep 2024
Publisher Packt
ISBN-13 9781803245744
Length 312 pages
Edition 2nd Edition
Languages
Concepts
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Authors (2):
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Saurabh Chakravarty Saurabh Chakravarty
Author Profile Icon Saurabh Chakravarty
Saurabh Chakravarty
Zhenya Antić Zhenya Antić
Author Profile Icon Zhenya Antić
Zhenya Antić
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Toc

Table of Contents (13) 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: Visualizing Text Data 8. Chapter 8: Transformers and Their Applications 9. Chapter 9: Natural Language Understanding 10. Chapter 10: Generative AI and Large Language Models 11. Index 12. Other Books You May Enjoy

Removing stopwords

When we work with words, especially if we are considering the words’ semantics, we sometimes need to exclude some very frequent words that do not bring any substantial meaning into the sentence (words such as but, can, we, etc.). For example, if we want to get a rough sense of the topic of a text, we could count its most frequent words. However, in any text, the most frequent words will be stopwords, so we want to remove them before processing. This recipe shows how to do that. The stopwords list we are using in this recipe comes from the NLTK package and might not include all the words you need. You will need to modify the list accordingly.

Getting ready

We will remove stopwords using spaCy and NLTK; these packages are part of the Poetry environment that we installed earlier.

We will be using the Sherlock Holmes text referred to earlier. For this recipe, we will need just the beginning of the book, which can be found in the file at https://github...

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