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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

Semantic similarity methods for semantic parsing

Natural language allows us to express the same concept in different ways and with different words. Every language has synonyms and semantically related words.

As an NLP developer, while developing a semantic parser for a chatbot application, text classification, or any other semantic application, you should keep in my mind that users use a fairly wide set of phrases and expressions for each intent. In fact, if you're building a chatbot by using a platform such as RASA (https://rasa.com/) or on a platform such as Dialogflow (https://dialogflow.cloud.google.com/), you're asked to provide as many utterance examples as you can provide for each intent. Then, these utterances are used to train the intent classifier behind the scenes.

There are usually two ways to recognize semantic similarity, either with a synonyms dictionary or with word vector-based semantic similarity methods. In this section, we will discuss both approaches...

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