Teaching Computers to Read
eBook - ePub

Teaching Computers to Read

Effective Best Practices in Building Valuable NLP Solutions

  1. English
  2. ePUB (mobile friendly)
  3. Available on iOS & Android
eBook - ePub

Teaching Computers to Read

Effective Best Practices in Building Valuable NLP Solutions

About this book

Building Natural Language Processing (NLP) solutions that deliver ongoing business value is not straightforward. This book provides clarity and guidance on how to design, develop, deploy, and maintain NLP solutions that address real-world business problems.

In this book, we discuss the main challenges and pitfalls encountered when building NLP solutions. We also outline how technical choices interact with (and are impacted by) data, tools, the business goals, and integration between human experts and the artificial intelligence (AI) solution. The best practices we cover here do not depend on cutting-edge modeling algorithms or the architectural flavor of the month. We provide practical advice for NLP solutions that are adaptable to the solution's specific technical building blocks.

Through providing best practices across the lifecycle of NLP development, this handbook will help organizations – particularly technical teams – use critical thinking to understand how, when, and why to build NLP solutions, what the common challenges are, and how to address or avoid those challenges. These best practices help organizations deliver consistent value to their stakeholders and deliver on the promise of AI and NLP.

A code companion for the book is available here: https://github.com/TeachingComputersToRead/TC2R-CodeCompanion

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Yes, you can access Teaching Computers to Read by Rachel Wagner-Kaiser in PDF and/or ePUB format, as well as other popular books in Economics & Artificial Intelligence (AI) & Semantics. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Cover Page
  2. Half-Title Page
  3. Title Page
  4. Copyright Page
  5. Contents
  6. Acronyms and Definitions
  7. Preface
  8. Acknowledgments
  9. Chapter 1 Debunking Common Myths in Natural Language Processing
  10. Chapter 2 The Trajectory of Natural Language Processing: Classic, Modern, and Generative
  11. Chapter 3 Large Language Models and Generative Artificial Intelligence
  12. Chapter 4 Pre-processing and Exploratory Data Analysis for NLP
  13. Chapter 5 Framing the Task and Data Labeling
  14. Chapter 6 Data Curation for NLP Corpora
  15. Chapter 7 Machine Learning Approaches for Natural Language Problems
  16. Chapter 8 Working Across Languages in NLP
  17. Chapter 9 Evaluating Performance of NLP Solutions
  18. Chapter 10 Maintaining Value: Deploying and Monitoring NLP Solutions
  19. Chapter 11 NLPOps: The Mechanics of NLP Production at Scale
  20. Chapter 12 Ethics in Data Science and NLP
  21. Chapter 13 Key Factors for Successful NLP Solutions
  22. Index