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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

Combining similar words – lemmatization

We can find the canonical form of the word using lemmatization. For example, the lemma of the word cats is cat, and the lemma for the word ran is run. This is useful when we are trying to match some word and don’t want to list out all the possible forms. Instead, we can just use its lemma.

Getting ready

We will be using the spaCy package for this recipe.

How to do it…

When the spaCy model processes a piece of text, the resulting Document object contains an iterator over the Token objects within it, as we saw in the Part of speech tagging recipe. These Token objects contain the lemma information for each word in the text.

Here are the steps for getting the lemmas:

  1. Import the file and language utils files. This will import spaCy and initialize the small_model object:
    %run -i "../util/file_utils.ipynb"
    %run -i "../util/lang_utils.ipynb"
  2. Create a list of words we want to lemmatize...
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