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  3. Programming for Corpus Linguistics with Python and Dataframes
  • Cited by 4
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    • Publisher:
      Cambridge University Press
      Publication date:
      May 2024
      June 2024
      ISBN:
      9781108904094
      9781009486781
      9781108822589
      DOI:
      https://doi.org/10.1017/9781108904094
      Dimensions:
      (229 x 152 mm)
      Weight & Pages:
      0.306kg, 114 Pages
      Dimensions:
      (229 x 152 mm)
      Weight & Pages:
      0.18kg, 114 Pages
      • Subjects:
      Research Methods in Linguistics, Applied Linguistics, Language and Linguistics
      Series:
      Elements in Corpus Linguistics
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    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

    Anthony, L. (2020). Programming for corpus linguistics. In Paquot, M. and Gries, S. T., eds. Practical Handbook of Corpus Linguistics. Springer, pp. 181–207.
    Biber, D., Conrad, S., & Cortes, V. (2004). If you look at … : Lexical bundles in university teaching and textbooks. Applied Linguistics, 25(3), 371–405.
    Biber, D., & Egbert, J. (2018). Register Variation Online. Cambridge University Press.
    Brezina, V. (2018). Statistics in Corpus Linguistics: A Practical Guide. Cambridge University Press.
    Dunning, T. E. (1993). Accurate methods for the statistics of surprise and coincidence. Computational Linguistics, 19(1), 61–74.
    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.
    Gabrielatos, C. (2018). Keyness analysis: Nature, metrics and techniques. In Taylor, C. & Marchi, A., eds. Corpus Approaches to Discourse: A Critical Review. Routledge, pp. 225–258.
    Hetland, M. L. (2014). Python Algorithms: Mastering Basic Algorithms in the Python Language. Apress.
    Honnibal, M., Montani, I., Van Landeghem, S., & Boyd, A. (2020). spaCy: Industrial-strength natural language processing in Python. https://spacy.io/
    Ide, N., & Suderman, K. (2004, May). The American National Corpus First Release. In Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04), Lisbon, Portugal. European Language Resources Association (ELRA). https://aclanthology.org/L04-1313/
    Lee, K. D., & Hubbard, S. H. (2015). Data Structures and Algorithms with Python. Springer.
    Nivre, J., Agić, Ž., Ahrenberg, L. et al. (2017). Universal Dependencies 2.1. https://universaldependencies.org/u/pos/
    Rayson, P. (n.d.). Log-likelihood and effect size calculator. http://ucrel.lancs.ac.uk/llwizard.html
    Rychlý, P. (2008). A lexicographer-friendly association score. Proceedings from Recent Advances in Slavonic Natural Language Processing (pp. 6–9). Karlova Studánka, Czech Republic: Masaryk University. nlp.fi.muni.cz/raslan/2008/raslan08.pdf

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