Natural Language Annotation for Machine Learning (PDF + ePub)
Natural Language Annotation for Machine Learning By James Pustejovsky, Amber Stubbs
Publisher: O'R[eil]ly Me[dia] 2012 | 350 Pages | ISBN: 1449306667 | EPUB + PDF | 3 MB + 2 MB
With this digital Early Release edition of Natural Language Annotation for Machine Learning, you get the entire book bundle in its earliest form – the author's raw and unedited content – so you can take advantage of this content long before the book's official release. You'll also receive updates when significant changes are made, as well as the final ebook version.
Create your own natural language training corpus for machine learning. This example-driven book walks you through the annotation cycle, from selecting an annotation task and creating the annotation specification to designing the guidelines, creating a "gold standard" corpus, and then beginning the actual data creation with the annotation process.
Systems exist for analyzing existing corpora, but making a new corpus can be extremely complex. To help you build a foundation for your own machine learning goals, this easy-to-use guide includes case studies that demonstrate four different annotation tasks in detail. You’ll also learn how to use a lightweight software package for annotating texts and adjudicating the annotations.
This book is a perfect companion to O'Reilly’s Natural Language Processing with Python, which describes how to use existing corpora with the Natural Language Toolkit.
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