Natural Language Annotation for Machine Learning
A Guide to Corpus-Building for Applications
$75.96
- Paperback
350 pages
- Release Date
20 November 2012
Summary
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.
Book Details
| ISBN-13: | 9781449306663 |
|---|---|
| ISBN-10: | 1449306667 |
| Author: | James Pustejovsky |
| Publisher: | O'Reilly Media |
| Imprint: | O'Reilly Media |
| Format: | Paperback |
| Number of Pages: | 350 |
| Edition: | annotated edition |
| Release Date: | 20 November 2012 |
| Weight: | 618g |
| Dimensions: | 24mm x 179mm x 233mm |
| Series: | Oreilly and Associate |
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James Pustejovsky
James Pustejovsky teaches and does research in Artificial Intelligence and Computational Linguistics in the Computer Science Department at Brandeis University. His main areas of interest include: lexical meaning, computational semantics, temporal and spatial reasoning, and corpus linguistics. He is active in the development of standards for interoperability between language processing applications, and lead the creation of the recently adopted ISO standard for time annotation, ISO-TimeML. He is currently heading the development of a standard for annotating spatial information in language. More information on publications and research activities can be found at his webpage: pusto.com. Amber Stubbs is a Ph.D. candidate in Computer Science at Brandeis University in the Laboratory for Linguistics and Computation. Her dissertation is focused on creating an annotation methodology to aid in extracting high-level information from natural language files, particularly biomedical texts. Information about her publications and other projects can be found on her website: /~astubbs/.
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