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Natural Language Annotation for Machine Learning
James Pustejovsky
(Author)
·
Amber Stubbs
(Author)
·
O'Reilly Media
· Paperback
Natural Language Annotation for Machine Learning - Pustejovsky, James ; Stubbs, Amber
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Synopsis "Natural Language Annotation for Machine Learning"
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 corpusBuild a model and specification for your annotation projectExamine the different annotation formats, from basic XML to the Linguistic Annotation FrameworkCreate a gold standard corpus that can be used to train and test ML algorithmsSelect the ML algorithms that will process your annotated dataEvaluate the test results and revise your annotation taskLearn how to use lightweight software for annotating texts and adjudicating the annotationsThis book is a perfect companion to O'Reilly's Natural Language Processing with Python.
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All books in our catalog are Original.
The book is written in English.
The binding of this edition is Paperback.
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