Natural Language Annotation for Machine Learning: A Guide to Corpus-Building for Applications
by James Pustejovsky, Amber Stubbs

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.

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Publisher: O'Reilly
Page Count (est.): 310
ISBN10: 1449306667
ISBN13: 9781449306663
Cover: Paperback
Pub Date: 11/1/2012