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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 2: Core Operations with spaCy

In this chapter, you will learn the core operations with spaCy, such as creating a language pipeline, tokenizing the text, and breaking the text into its sentences.

First, you'll learn what a language processing pipeline is and the pipeline components. We'll continue with general spaCy conventions – important classes and class organization – to help you to better understand spaCy library organization and develop a solid understanding of the library itself.

You will then learn about the first pipeline component – Tokenizer. You'll also learn about an important linguistic concept – lemmatization – along with its applications in natural language understanding (NLU). Following that, we will cover container classes and spaCy data structures in detail. We will finish the chapter with useful spaCy features that you'll use in everyday NLP development.

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