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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

Introducing NER

We opened this chapter with a tagger, and we'll see another very handy tagger—the NER tagger of spaCy. As NER's name suggests, we are interested in finding named entities.

What is a named entity? A named entity is a real-world object that we can refer to by a proper name or a quantity of interest. It can be a person, a place (city, country, landmark, famous building), an organization, a company, a product, dates, times, percentages, monetary amounts, a drug, or a disease name. Some examples are Alicia Keys, Paris, France, Brandenburg Gate, WHO, Google, Porsche Cayenne, and so on.

A named entity always points to a specific object, and that object is distinguishable via the corresponding named entity. For instance, if we tag the sentence Paris is the capital of France, we parse Paris and France as named entities, but not the word capital. The reason is that capital does not point to a specific object; it's a general name for many objects.

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