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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.


A Guide to Corpus-Building for Applications
By James Pustejovsky, Amber Stubbs
Publisher: O'Reilly Media
Released: October 2012
Pages: 346
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