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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

Getting the dataset and evaluation baseline ready

Classifying texts is a classic NLP problem. This NLP task involves assigning a value to a text, for example, a topic or a sentiment, and any such task requires evaluation. In this recipe, we will load a dataset, prepare it for processing, and create an evaluation baseline. The recipe builds on some of the recipes from Chapter 3, Representing Text: Capturing Semantics, where we used different tools to represent text in a computer-readable form.

Getting ready

For most recipes in this chapter, we will use the BBC News dataset, which contains text from five topics: business, entertainment, politics, sport, and tech. The dataset is located in the bbc-text.csv file in this chapter's GitHub directory.

In this recipe, we will need two additional packages, numpy and sklearn. Install them using pip:

pip install numpy
pip install sklearn

How to do it…

In this recipe, we will be classifying just two of the five topics...

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