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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
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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

Extracting keywords

In this recipe, we will extract keywords from a text. We will be working with the BBC news dataset that contains news articles. You can learn more about the dataset in Chapter 4, in the recipe titled Clustering sentences using K-Means: unsupervised text classification.

Extracting keywords from text can give us a quick idea about what the article is about and can also serve as a basis for a tagging system, for example, on a website.

For the extraction to work correctly, we need to train a TF-IDF vectorizer that we will use during the extraction phase.

Getting ready

In this recipe, we will use the sklearn package. It is part of the Poetry environment. You can also install it together with other packages by installing the requirements.txt file.

The BBC news dataset is available on Hugging Face at https://huggingface.co/datasets/SetFit/bbc-news.

The notebook is located at https://github.com/PacktPublishing/Python-Natural-Language-Processing-Cookbook...

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