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  3. Programming for Corpus Linguistics with Python and Dataframes
  • Additional resources for this publication available here.
  • Cited by 3
Publisher:
Cambridge University Press
Online publication date:
May 2024
Print publication year:
2024
Online ISBN:
9781108904094
DOI:
https://doi.org/10.1017/9781108904094
Subjects:
Research Methods in Linguistics, Applied Linguistics, Language and Linguistics
Series:
Elements in Corpus Linguistics
Information

Book description

This Element offers intermediate or experienced programmers algorithms for Corpus Linguistic (CL) programming in the Python language using dataframes that provide a fast, efficient, intuitive set of methods for working with large, complex datasets such as corpora. This Element demonstrates principles of dataframe programming applied to CL analyses, as well as complete algorithms for creating concordances; producing lists of collocates, keywords, and lexical bundles; and performing key feature analysis. An additional algorithm for creating dataframe corpora is presented including methods for tokenizing, part-of-speech tagging, and lemmatizing using spaCy. This Element provides a set of core skills that can be applied to a range of CL research questions, as well as to original analyses not possible with existing corpus software.

References

References

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Biber, D., Conrad, S., & Cortes, V. (2004). If you look at … : Lexical bundles in university teaching and textbooks. Applied Linguistics, 25(3), 371–405.
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Brezina, V. (2018). Statistics in Corpus Linguistics: A Practical Guide. Cambridge University Press.
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Egbert, J., & Biber, D. (2019). Incorporating text dispersion into keyword analyses. Corpora, 14(1), 77–104.
Egbert, J., & Biber, D. (2023). Key feature analysis: A simple, yet powerful method for comparing text varieties. Corpora, 18(1), 121–133.
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