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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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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

Using dependency relations for intent recognition

After extracting the entities, we want to find out what sort of intent the user carries – to book a flight, to purchase a meal on their already booked flight, cancel their flight, and so on. If you look at the intents list again, you will see that every intent includes a verb (to book) and an object that the verb acts on (flight, hotel, meal).

In this section, we'll extract transitive verbs and their direct objects from utterances. We'll begin our intent recognition section by extracting the transitive verb and the direct object of the verb. Then, we'll explore how to understand a user's intent by recognizing synonyms of verbs and nouns. Finally, we'll see how to determine a user's intent with semantic similarity methods. Before we move on to extracting transitive verbs and their direct objects, let's first quickly go over the concepts of transitive verbs and direct/indirect objects.

Linguistic...

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