Natural Language Annotation For Machine Learning 1st Edition James Pustejovsky Amber Stubbs by James Pustejovsky; Amber Stubbs 978-1-449-30666-3 (Paperback), 1-449-30666-7 (Paperback), 978-1-449-30666-3 (PAPERBACK), 1-449-30666-7 (PAPERBACK) instant download after payment.

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 

James Pustejovsky is a Professor at Brandeis University, where he teaches and researches artificial intelligence and computational linguistics in the Computer Science Department. 

Amber Stubbs recently completed a Ph.D. at Brandeis University on the topic of annotation methodologies. She is now a Postdoctoral Associate at SUNY Albany.