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OverviewCorpus 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. Full Product DetailsAuthor: Jonathan Dunn (University of Canterbury, Christchurch, New Zealand)Publisher: Cambridge University Press Imprint: Cambridge University Press Edition: New edition Dimensions: Width: 15.20cm , Height: 0.50cm , Length: 22.90cm Weight: 0.142kg ISBN: 9781009074438ISBN 10: 1009074431 Pages: 75 Publication Date: 31 March 2022 Audience: General/trade , General Format: Paperback Publisher's Status: Active Availability: Manufactured on demand ![]() We will order this item for you from a manufactured on demand supplier. Table of ContentsReviewsAuthor InformationTab Content 6Author Website:Countries AvailableAll regions |