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Mastering spaCy

You're reading from Mastering spaCy Build structured NLP solutions with custom components and models powered by spacy-llm

Product type Paperback

Published in Feb 2025

Publisher Packt

ISBN-13 9781835880463

Length 238 pages

Edition 2nd Edition

Languages

Python

Tools

Spacy

Concepts

GPT/LLMs

Authors (2):

Déborah Mesquita

Duygu Altınok

View More author details

Table of Contents (17) Chapters

Preface

1. Part 1: Getting Started with spaCy

2. Chapter 1: Getting Started with spaCy FREE CHAPTER

3. Chapter 2: Core Operations with spaCy

4. Part 2: Advanced Linguistic and Semantic Analysis

5. Chapter 3: Extracting Linguistic Features

6. Chapter 4: Mastering Rule-Based Matching

7. Chapter 5: Extracting Semantic Representations with spaCy Pipelines

8. Chapter 6: Utilizing spaCy with Transformers

9. Part 3: Customizing and Integrating NLP Workflows

10. Chapter 7: Enhancing NLP Tasks Using LLMs with spacy-llm

11. Chapter 8: Training an NER Component with Your Own Data

12. Chapter 9: Creating End-to-End spaCy Workflows with Weasel

13. Chapter 10: Training an Entity Linker Model with spaCy

14. Chapter 11: Integrating spaCy with Third-Party Libraries

15. Index

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16. Other Books You May Enjoy

Summary

In this chapter, we learned how to train an EntityLinker component using spaCy. We saw some implementation details to learn more about the HashEmbedCNN.v2 layer and the EntityLinker.v2 architecture.

We also discussed some characteristics of high-quality datasets for NLP training, stressing the importance of consistency, representativeness, and thorough documentation. Finally, we saw how to create a custom corpus reader used to train the entity linking model. With this knowledge, you can customize any other spaCy component.

In the next and final chapter, you will learn how to combine spaCy with other cool open source libraries to create great NLP applications. See you there!

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Authors (2)

Déborah Mesquita

Déborah Mesquita

Déborah is a data science consultant and writer. With a BSc in Computer Science from UFPE, one of Brazil's top computer science programs, she brings a diversified skill set refined through hands-on experience with various technologies. Déborah has thrived in different data science projects, including roles such as lead data scientist and technical contributor for respected publications. Her ability to translate complex concepts into simple language, coupled with her quick learning and broad vision, make her an effective educator. Actively engaged in community initiatives, she works to ensure equitable access to knowledge, reflecting her belief that technology is not a panacea, but a powerful tool for societal improvement when used for that purpose.

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Duygu Altınok

Duygu Altınok

Duygu Altınok is a senior NLP engineer with 12 years of experience in almost all areas of NLP including search engine technology, speech recognition, text analytics, and conversational AI. She authored several publications in the NLP area at conferences such as LREC and CLNLP. She also enjoys working on open-source projects and is a contributor to the spaCy library. Duygu earned her undergraduate degree in Computer Engineering from METU, Ankara in 2010 and later earned her Master's degree in Mathematics from Bilkent University, Ankara in 2012. She is currently a senior engineer at German Autolabs with a focus on conversational AI for voice assistants. Originally from Istanbul, Duygu currently resides in Berlin, DE with her cute dog Adele.

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