Natural Language Annotation for Machine Learning: A Guide to Corpus-Building for Applications
Create your own natural language training corpus for machine learning. Whether you’re working with English, Chinese, or any other natural language, this hands-on book guides you through a proven annotation development cycle—the process of adding metadata to your training corpus to help ML algorithms work more efficiently. You don’t need any programming or linguistics experience to get started.
Using detailed examples at every step, you’ll learn how the MATTER Annotation Development Process helps you Model, Annotate, Train, Test, Evaluate, and Revise your training corpus. You also get a complete walkthrough of a real-world annotation project.
This book is a perfect companion to O’Reilly’s Natural Language Processing with Python.
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Chapter 2 Defining Your Goal and Dataset
Chapter 3 Corpus Analytics
Chapter 4 Building Your Model and Specification
Chapter 5 Applying and Adopting Annotation Standards
Chapter 6 Annotation and Adjudication
Chapter 8 Testing and Evaluation
Chapter 9 Revising and Reporting
About the Authors