Natural Language Annotation for Machine Learning: A Guide to Corpus-Building for Applications - Tapa blanda

Pustejovsky, James

 
9781449306663: Natural Language Annotation for Machine Learning: A Guide to Corpus-Building for Applications
Tapa blanda
ISBN 10: 1449306667 ISBN 13: 9781449306663
Editorial: O'Reilly Media, 2012

Sinopsis

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.

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Acerca del autor

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: http://pages.cs.brandeis.edu/~astubbs/.

"Sobre este título" puede pertenecer a otra edición de este libro.

Editorial
O'Reilly Media
Año de publicación
2012
Idioma
Inglés
ISBN 10 
1449306667
ISBN 13 
9781449306663
Encuadernación
Tapa blanda
Número de páginas
350
Contacto del fabricante
no disponible
Persona responsable
no disponible

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