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Mastering spaCy

You're reading from   Mastering spaCy An end-to-end practical guide to implementing NLP applications using the Python ecosystem

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Product type Paperback
Published in Jul 2021
Publisher Packt
ISBN-13 9781800563353
Length 356 pages
Edition 1st Edition
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Author (1):
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Duygu Altınok Duygu Altınok
Author Profile Icon Duygu Altınok
Duygu Altınok
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Table of Contents (15) Chapters Close

Preface 1. Section 1: Getting Started with spaCy
2. Chapter 1: Getting Started with spaCy FREE CHAPTER 3. Chapter 2: Core Operations with spaCy 4. Section 2: spaCy Features
5. Chapter 3: Linguistic Features 6. Chapter 4: Rule-Based Matching 7. Chapter 5: Working with Word Vectors and Semantic Similarity 8. Chapter 6: Putting Everything Together: Semantic Parsing with spaCy 9. Section 3: Machine Learning with spaCy
10. Chapter 7: Customizing spaCy Models 11. Chapter 8: Text Classification with spaCy 12. Chapter 9: spaCy and Transformers 13. Chapter 10: Putting Everything Together: Designing Your Chatbot with spaCy 14. Other Books You May Enjoy

Chapter 8: Text Classification with spaCy

This chapter is devoted to a very basic and popular task of NLP: text classification. You will first learn how to train spaCy's text classifier component, TextCategorizer. For this, you will learn how to prepare data and feed the data to the classifier; then we'll proceed to train the classifier. You'll also practice your new TextCategorizer skills on a popular dataset for sentiment analysis.

Next, you will also do text classification with the popular framework TensorFlow's Keras API together with spaCy. You will learn the basics of neural networks, sequential data modeling with LSTMs, and how to prepare text for machine learning tasks with Keras's text preprocessing module. You will also learn how to design a neural network with tf.keras.

Following that, we will then make an end-to-end text classification experiment, from data preparation to preprocessing text with Keras Tokenizer, neural network designing,...

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