Natural Language Annotation for Machine Learning: A Guide to Corpus-Building for Applications, 1st Edition eBook

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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.

This is a digital product.

Natural Language Annotation for Machine Learning: A Guide to Corpus-Building for Applications 1st Edition is written by James Pustejovsky; Amber Stubbs and published by O’Reilly Media. The Digital and eTextbook ISBNs for Natural Language Annotation for Machine Learning are 9781449359768, 1449359760 and the print ISBNs are 9781449306663, 1449306667. Additional ISBNs for this eTextbook include 9781449359775, 9781449359751.