Natural Language Annotation for Machine Learning

James Pustejovsky

ISBN 10: 1449306667 / ISBN 13: 9781449306663

Verlag: O'Reilly Media, US, 2012

Beschreibung

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.

Bestandsnummer des Verkäufers LU-9781449306663

Bibliografische Details

• Titel: Natural Language Annotation for Machine Learning
• Verlag: O'Reilly Media, US
• Erscheinungsjahr: 2012
• Sprache: Englisch
• ISBN-10: 1449306667
• ISBN-13: 9781449306663
• Einband: Paperback
• Zustand: New

Über diesen Titel

Inhaltsangabe

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.

Über die Autorin bzw. den 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/.