Before you leave...
Take 20% off your first order
20% off
Enter the code below at checkout to get 20% off your first order
Discover summer reading lists for all ages & interests!
Find Your Next Read

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.
Author: James Pustejovsky, Amber Stubbs
ISBN-10: 1449306667
ISBN-13: 9781449306663
Publisher: O'Reilly Media
Language: English
Published: 12/04/2012
Pages: 339
Format: Paperback
Weight: 1.21lbs
Size: 9.01h x 7.07w x 0.73d
James Pustejovsky teaches and does research in Artificial Intelligence and Computational Linguistics in the Computer Science Department at Brandeis University. His main areas of interest include: lexical meaning, computational semantics, temporal and spatial reasoning, and corpus linguistics. He is active in the development of standards for interoperability between language processing applications, and lead the creation of the recently adopted ISO standard for time annotation, ISO-TimeML. He is currently heading the development of a standard for annotating spatial information in language. More information on publications and research activities can be found at his webpage: pusto.com.
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/
Thanks for subscribing!
This email has been registered!
Take 20% off your first order
Enter the code below at checkout to get 20% off your first order