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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

Summary

We have now reached the end of an exhaustive chapter of spaCy core operations and the basic features of spaCy. This chapter gave you a comprehensive picture of spaCy library classes and methods. We made a deep dive into language processing pipelining and learned about pipeline components. We also covered a basic yet important syntactic task: tokenization. We continued with the linguistic concept of lemmatization and you learned a real-world application of a spaCy feature. We explored spaCy container classes in detail and finalized the chapter with precise and useful spaCy features. At this point, you have a good grasp of spaCy language pipelining and you are confident about accomplishing bigger tasks.

In the next chapter, we will dive into spaCy's full linguistic power. You'll discover linguistic features including spaCy's most used features: the POS tagger, dependency parser, named entities, and entity linking.

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