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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

Introduction to dependency parsing

If you are already familiar with spaCy, you must have come across the spaCy dependency parser. Though many developers see dependency parser on the spaCy documentation, they're shy about using it or don't know how to use this feature to the fullest. In this part, you'll explore a systematic way of representing a sentence syntactically. Let's start with what dependency parsing actually is.

What is dependency parsing?

In the previous section, we focused on POS tags—syntactic categories of words. Though POS tags provide information about neighbor words' tags as well, they do not give away any relations between words that are not neighbors in the given sentence.

In this section, we'll focus on dependency parsing—a more structured way of exploring the sentence syntax. As the name suggests, dependency parsing is related to analyzing sentence structures via dependencies between the tokens. A dependency...

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