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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

Advanced semantic similarity methods

In this section, we'll discover advanced semantic similarity methods for word, phrase, and sentence similarity. We've already learned how to calculate semantic similarity with spaCy's similarity method and obtained some scores. But what do these scores mean? How are they calculated? Before we look at more advanced methods, first, we'll learn how semantic similarity is calculated.

Understanding semantic similarity

When we collect text data (any sort of data), we want to see how some examples are similar, different, or related. We want to measure how similar two pieces of text are by calculating their similarity scores. Here, the term semantic similarity comes into the picture; semantic similarity is a metric that's defined over texts, where the distance between two texts is based on their semantics.

A metric in mathematics is basically a distance function. Every metric induces a topology on the vector space. Word...

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