Natural Language Annotation for Machine Learning

10/29/2012 · No Responses

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.

Define a clear annotation goal before collecting your dataset (corpus)
Learn tools for analyzing the linguistic content of your corpus
Build a model and specification for your annotation project
Examine the different annotation formats, from basic XML to the Linguistic Annotation Framework
Create a gold standard corpus that can be used to train and test ML algorithms
Select the ML algorithms that will process your annotated data
Evaluate the test results and revise your annotation task
Learn how to use lightweight software for annotating texts and adjudicating the annotations

This book is a perfect companion to O’Reilly’s Natural Language Processing with Python.

Table of Contents
Chapter 1. The Basics
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 7. Training: Machine Learning
Chapter 8. Testing and Evaluation
Chapter 9. Revising and Reporting
Chapter 10. Annotation: TimeML
Chapter 11. Automatic Annotation: Generating TimeML
Chapter 12. Afterword: The Future of Annotation

Appendix A. List of Available Corpora and Specifications
Appendix B. List of Software Resources
Appendix C. MAE User Guide
Appendix D. MAI User Guide
Appendix E. Bibliography