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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
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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

spaCy container objects

At the beginning of this chapter, we saw a list of container objects including Doc, Token, Span, and Lexeme. We already used Token and Doc in our code. In this subsection, we'll see the properties of the container objects in detail.

Using container objects, we can access the linguistic properties that spaCy assigns to the text. A container object is a logical representation of the text units such as a document, a token, or a slice of the document.

Container objects in spaCy follow the natural structure of the text: a document is composed of sentences and sentences are composed of tokens.

We most widely use Doc, Token, and Span objects in development, which represent a document, a single token, and a phrase, respectively. A container can contain other containers, for instance a document contains tokens and spans.

Let's explore each class and its useful properties one by one.

Doc

We created Doc objects in our code to represent the...

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