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Commentary published In:
Journal of Second Language Studies
Vol. 6:1 (2023) ► pp.29–60
Commentary

How can we communicate (visually) what we (usually) mean by collocation and keyness?

A visual response to Gries (2022a)

ORCID logo | Xi’an Jiaotong-Liverpool University , China

Corpus linguistic methods can now be easily employed in a wide range of studies within sub-disciplines of linguistics and well beyond. In a two-part paper, Gries (2022a, 2022b) challenges some of the most widely used ‘association measures’ of what many might feel to be powerful aspects of text patterning: collocation and key words. While the additional association measure offers some new possibilities, this paper highlights the strong influence of another frequency parameter on odds ratio and Gries’s suggested association measure, and questions the applicability of his cautions for many different kinds of corpus research. Nevertheless, having been inspired to look at different aspects of association and dispersion more carefully, the author presents some new visualizations which were designed to communicate some of the important lessons to be learned from Gries’s papers, especially for learners and teachers using corpus tools in Second Language classrooms.

Keywords: association, frequency, collocation, keyness, dispersion, log-likelihood, range, Gries’s DP
Article outline
  • 1.Introduction
  • 2.Definitions
  • 3.Re-examining the roles of inputs for collocation measures
  • 4.Keeping the Zipfian curve in mind for key words
  • 5.Re-examining issues of dispersion measures
  • 6.Conclusion
  • Note
  • References
Published online: 13 February 2023
DOI logo
https://doi.org/10.1075/jsls.22019.jea
References
Anthony, L.
(2022) AntConc (Version 4.0.1). Tokyo, Japan: Waseda University. Retrieved from [URL]
Bestgen, Yves & Sylviane Granger
2014 Quantifying the development of phraseological competence in L2 English writing: An automated approach. Journal of Second Language Writing 261. 28–41. DOI logoGoogle Scholar
Brezina, V., McEnery, T., & Wattam, S.
(2015) Collocations in context: A new perspective on collocation networks. International Journal of Corpus Linguistics, 20 (2), 139–173. DOI logoGoogle Scholar
BNC
(2007) The British National Corpus (Version 3 BNC XML ed.): Oxford University Computing Services on behalf of the BNC Consortium. URL: [URL]
Croft, W. B., Metzler, D., & Strohman, T.
(2010) Search Engines: Information Retrieval in Practice. Boston: Addison-Wesley.Google Scholar
Dunning, T.
(1993) Accurate methods for the statistics of surprise and coincidence. Computational Linguistics, 19 (1), 61–74.Google Scholar
Evert, Stefan & Brigitte Krenn
(2001) Methods for the qualitative evaluation of lexical association measures. Proceedings of the 39th Annual Meeting of the Association for Computational Linguistics, p, 188–195. DOI logoGoogle Scholar
Garside, R., & Smith, N.
(1997) A hybrid grammatical tagger: CLAWS4. In R. Garside, G. Leech & A. McEnery (Eds.), Corpus Annotation: Linguistic Information from Computer Text Corpora (pp. 102–121). London: Longman. DOI logoGoogle Scholar
Gries, S. T.
(2013) 50-something years of work on collocations. International Journal of Corpus Linguistics, 18 (1), 137–165. DOI logoGoogle Scholar
Gries
(2015) The most under-used statistical method in corpus linguistics: multi-level (and mixed-effects) models. Corpora, 10(1), 95–125. DOI logoGoogle Scholar
Gries, S.
(2022a) What do (some of) our association measures measure (most)? Association? Journal of Second Language Studies, 5 (1). DOI logoGoogle Scholar
(2022b) What do (most of) our dispersion measures measure (most)? Dispersion? Journal of Second Language Studies. DOI logoGoogle Scholar
Hann, M. N.
(1973) The Statistical Force of Random Distribution. International Journal of Applied Linguistics, 20 1, 31–44. DOI logoGoogle Scholar
Hardie, A.
(2012) CQPweb: Combining Power, Flexibility and Usability in a Corpus Analysis Tool. International Journal of Corpus Linguistics, 17 (3), 380–409. DOI logoGoogle Scholar
Heaps, H.
(1978) Information retrieval: Computational and theoretical aspects. New York: Academic Press.Google Scholar
Hoey, M.
(2005) Lexical Priming: A New Theory of Words and Language. London: Routledge.Google Scholar
(2014) Words and their neighbours. In J. R. Taylor (Ed.), Oxford Handbook of the Word. Oxford: Oxford University Press.Google Scholar
Hunston, S.
(2002) Corpora in Applied Linguistics. Cambridge: Cambridge University Press. DOI logoGoogle Scholar
Jeaco, S.
(2017) Concordancing Lexical Primings: The rationale and design of a user-friendly corpus tool for English language teaching and self-tutoring based on the Lexical Priming theory of language. In M. Pace-Sigge & K. J. Patterson (Eds.), Lexical Priming: Applications and Advances (pp. 273–296). Amsterdam: John Benjamins. DOI logoGoogle Scholar
(2020) Key words when text forms the unit of study: Sizing up the effects of different measures. International Journal of Corpus Linguistics, 25(2), 125–154. DOI logoGoogle Scholar
Kilgarriff, A., Rychly, P., Smrz, P., & Tugwell, D.
(2004) The Sketch Engine. Paper presented at the 2003 International Conference on Natural Language Processing and Knowledge Engineering, Beijing.
Mahlberg, M.
(2013) Corpus stylistics and Dickens’s fiction: New York; Routledge 2013 DOI logoGoogle Scholar
Oakes, M. P.
(1998) Statistics for Corpus Linguistics. Edinburgh: Edinburgh University Press.Google Scholar
O’Keeffe, A., McCarthy, M., & Carter, R.
(2007) From Corpus to Classroom: Language Use and Language Teaching. Cambridge: Cambridge University Press. DOI logoGoogle Scholar
Rayson, P., & Garside, R.
(2000) Comparing corpora using frequency profiling. Paper presented at the Workshop on Comparing Corpora, Hong Kong University of Science and Technology, Hong Kong.
Read, T. R. C., & Cressie, N. A. C.
(1988) Goodness-of-fit Statistics for Discrete Multivariate Data. New York: Springer-Verlag. DOI logoGoogle Scholar
RStudio Team
(2022) RStudio: Integrated Development Environment for R. Boston, MA:PBC. Retrieved from [URL]
Rychlý, P.
(2008) A lexicographer-friendly association score. Paper presented at the Recent Advances in Slavonic Natural Language Processing Conference, Masaryk University, Brno.
Scott, M.
(1997) PC analysis of key words – and key key words. System, 25 (2), 233–245. DOI logoGoogle Scholar
Scott, M., & Tribble, C.
(2006) Textual Patterns: Key Words and Corpus Analysis in Language Education. Amsterdam: John Benjamins. DOI logoGoogle Scholar
Scott, M.
(2020) WordSmith Tools (Version 8). Oxford: Oxford University Press.Google Scholar
(2022) WordSmith Tools online manual “KeyWords: calculation”. Retrieved 31 October, 2022, from [URL]
Sinclair, J. M.
(1991) Corpus, Concordance, Collocation. Oxford: Oxford University Press.Google Scholar
(2004) New evidence, new priorities, new attitudes. In J. M. Sinclair (Ed.), How to Use Corpora in Language Teaching (pp. 271–299). Amsterdam: John Benjamins. DOI logoGoogle Scholar
Wermter, J., & Hahn, U.
(2006) You can’t beat frequency (unless you use linguistic knowledge): A qualitative evaluation of association measures for collocation and term extraction. Paper presented at the Annual Meeting of the Association for Computational Linguistics, Sydney. DOI logo
Wood, S. N.
(2011) Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. Journal of the Royal Statistical Society (B) 73(1):3–36. DOI logoGoogle Scholar
Zipf, G. K.
(1935) The Psycho-Biology of Language: An Introduction to Dynamic Philology. Boston, MA: Houghton Mifflin.Google Scholar