Natural Language Annotation for Machine Learning

A Guide to Corpus-Building for Applications

Nonfiction, Computers, Advanced Computing, Natural Language Processing, Database Management, Data Processing, General Computing
Cover of the book Natural Language Annotation for Machine Learning by James Pustejovsky, Amber Stubbs, O'Reilly Media
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Author: James Pustejovsky, Amber Stubbs ISBN: 9781449359768
Publisher: O'Reilly Media Publication: October 11, 2012
Imprint: O'Reilly Media Language: English
Author: James Pustejovsky, Amber Stubbs
ISBN: 9781449359768
Publisher: O'Reilly Media
Publication: October 11, 2012
Imprint: O'Reilly Media
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

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