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Press Release: October 30, 2012

Natural Language Annotation for Machine Learning--New from O'Reilly Media: A Guide to Corpus-Building for Applications

Natural Language Annotation for Machine Learning

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Sebastopol, CA—Create your own natural language training corpus for machine learning with Natural Language Annotation for Machine Learning ($39.99, O'Reilly Media). 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.

For a review copy or more information please email reviews@oreilly.com. Please include your delivery address and contact information.

About the Authors

James Pustejovsky teaches and does research in Artificial Intelligence and Computational Linguistics in the Computer Science Department at Brandeis University. More information on publications and research activities can be found at his webpage: pusto.com.

View James Pustejovsky's full profile page.

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/

View Amber Stubbs's full profile page.

Additional Resources
For more information about the book, including table of contents, author bios, and cover graphic, see: http://shop.oreilly.com/product/0636920020578.do


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