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Series:   Elements in Corpus Linguistics

Natural Language Processing for Corpus Linguistics

Published online by Cambridge University Press:  04 March 2022

Jonathan Dunn
Jonathan Dunn
Affiliation:
University of Canterbury, Christchurch, New Zealand

Summary

Corpus analysis can be expanded and scaled up by incorporating computational methods from natural language processing. This Element shows how text classification and text similarity models can extend our ability to undertake corpus linguistics across very large corpora. These computational methods are becoming increasingly important as corpora grow too large for more traditional types of linguistic analysis. We draw on five case studies to show how and why to use computational methods, ranging from usage-based grammar to authorship analysis to using social media for corpus-based sociolinguistics. Each section is accompanied by an interactive code notebook that shows how to implement the analysis in Python. A stand-alone Python package is also available to help readers use these methods with their own data. Because large-scale analysis introduces new ethical problems, this Element pairs each new methodology with a discussion of potential ethical implications.
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Keywords

computational linguisticsnatural language processingcorpus linguisticstext classificationtext similarityusage-based grammarcorpus-based sociolinguisticscomputational stylisticscomputational syntax
Type
Element
Information
Series: Elements in Corpus Linguistics
Online ISBN: 9781009070447
Publisher: Cambridge University Press
Print publication: 31 March 2022

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Natural Language Processing for Corpus Linguistics
  • Jonathan Dunn, University of Canterbury, Christchurch, New Zealand
  • Online ISBN: 9781009070447
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Natural Language Processing for Corpus Linguistics
  • Jonathan Dunn, University of Canterbury, Christchurch, New Zealand
  • Online ISBN: 9781009070447
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Natural Language Processing for Corpus Linguistics
  • Jonathan Dunn, University of Canterbury, Christchurch, New Zealand
  • Online ISBN: 9781009070447
Available formats
×