Natural Language Annotation for Machine Learning

Natural Language Annotation for Machine Learning

James Pustejovsky, Amber Stubbs

Language: English

Pages: 342

ISBN: 1449306667

Format: PDF / Kindle (mobi) / ePub

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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Http:// If not specifically stated in the examples, it should be assumed that the command import nltk was used prior to all sample code. Conventions Used in This Book The following typographical conventions are used in this book: Italic Indicates new terms, URLs, email addresses, filenames, and file extensions. Constant width Used for program listings, as well as within paragraphs to refer to program elements such as variable or function.

Language: Language-independent URL: WebAnnotator Modality: Websites Use: Web annotation Language: Language-independent URL: WordAligner Modality: Written Use: Machine Translation word alignment Language: Language-independent URL: Automated Annotation Tools Multipurpose tools fnTBL Modality: Written Use: Part-of-speech tagging, base.

Describe the traits that should be targeted when building a corpus. Because a corpus must always be a selected subset of any chosen language, it cannot contain all examples of the language’s possible uses. Therefore, a corpus must be created by sampling the existing texts of a language. Since any sampling procedure inherently contains the possibility of skewing the dataset, care should be taken to ensure that the corpus is representative of the “full range of variability in a population” (Biber.

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Than you initially thought. Summary In this chapter we defined what models and specifications are, and looked at some of the factors that should be taken into account when creating a model and spec for your own annotation task. Specifically, we discussed the following: The model of your annotation project is the abstract representation of your goal, and the specification is the concrete representation of it. XML DTDs are a handy way to represent a specification; they can be applied.

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