Natural Language Annotation for Machine Learning: A guide to corpus-building for applications

✍ Scribed by James Pustejovsky, Amber Stubbs

• Publisher: O'Reilly Media
• Year: 2012
• Tongue: English
• Leaves: 350
• Edition: Early Release
• Category: Library

✦ Synopsis

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.