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Natural Language Annotation for Machine Learningby James Pustejovsky
Synopses & Reviews
Create your own natural language training corpus for machine learning. Whether youre 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 dont need any programming or linguistics experience to get started.
Using detailed examples at every step, youll 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.
This book is a perfect companion to OReillys Natural Language Processing with Python.
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
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