Natural Language Annotation for Machine Learning: A Guide to Corpus-Building for Applications
Stubbs, Amber,Pustejovsky, James
Language: English
Published by O'Reilly Media, 2012
- Softcover
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- Title
- Natural Language Annotation for Machine Learning: A Guide to Corpus-Building for Applications
- Author
- Stubbs, Amber,Pustejovsky, James
- Publisher
- O'Reilly Media
- Publication year
- 2012
- Condition
- Acceptable
- Binding
- Paperback
- Language
- English
- ISBN 10
- 1449306667
- ISBN 13
- 9781449306663
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.
"Synopsis" may belong to another edition of this title.
About the Author
Amber Stubbs recently completed her Ph.D. in Computer Science at Brandeis University, and is currently a Postdoctoral Associate at SUNY Albany. Her dissertation focused on creating an annotation methodology to aid in extracting high-level information from natural language files, particularly biomedical texts. Her website can be found at http: //pages.cs.brandeis.edu/ astubbs/
"About the title" may belong to another edition of this title.
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