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 a...

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Bibliographische Detailangaben

1. Verfasser:	Dunn, Jonathan ca. 20./21. Jh (VerfasserIn)
Format:	Elektronisch E-Book
Sprache:	English
Veröffentlicht:	Cambridge Cambridge University Press 2022
Schriftenreihe:	Cambridge elements
Schlagworte:	Corpora (Linguistics) / Data processing Natural language processing (Computer science) Automatische Sprachanalyse Korpus > Linguistik
Online-Zugang:	DE-12 DE-473 Volltext
Zusammenfassung:	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
Beschreibung:	Title from publisher's bibliographic system (viewed on 04 Mar 2022)
Beschreibung:	1 Online-Ressource (84 Seiten)
ISBN:	9781009070447
DOI:	10.1017/9781009070447