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Natural Language Annotation for Machine Learning

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Natural Language Annotation for Machine Learning Cover

 

Synopses & Reviews

Publisher Comments:

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. Youll also learn how to use a lightweight software package for annotating texts and adjudicating the annotations.

This book is a perfect companion to O'Reillys Natural Language Processing with Python, which describes how to use existing corpora with the Natural Language Toolkit.

About the Author

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.

Table of Contents

PrefaceChapter 1: The BasicsChapter 2: Defining Your Goal and DatasetChapter 3: Corpus AnalyticsChapter 4: Building Your Model and SpecificationChapter 5: Applying and Adopting Annotation StandardsChapter 6: Annotation and AdjudicationChapter 7: Training: Machine LearningChapter 8: Testing and EvaluationChapter 9: Revising and ReportingChapter 10: Annotation: TimeMLChapter 11: Automatic Annotation: Generating TimeMLChapter 12: Afterword: The Future of AnnotationList of Available Corpora and SpecificationsList of Software ResourcesMAE User GuideMAI User GuideBibliographyColophon

Product Details

ISBN:
9781449306663
Author:
Pustejovsky, James
Publisher:
O'Reilly Media
Author:
Stubbs, Amber
Subject:
Natural Language Processing
Subject:
Database design
Subject:
algorithm;analysis;annotation;corpus;data;machine learning;n-grams;natural language processing
Edition Description:
Trade Paper
Publication Date:
20121131
Binding:
TRADE PAPER
Language:
English
Pages:
342
Dimensions:
9.19 x 7 in

Related Subjects

Computers and Internet » Artificial Intelligence » Natural Language
Computers and Internet » Computers Reference » General
Computers and Internet » Database » Design
Computers and Internet » Software Engineering » Programming and Languages
Science and Mathematics » Mathematics » General

Natural Language Annotation for Machine Learning New Trade Paper
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Product details 342 pages O'Reilly Media - English 9781449306663 Reviews:
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