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Licensed Unlicensed Requires Authentication Published by De Gruyter (A) December 5, 2017

Application of modern classification methods in the study of bilingualism

  • András Vargha and Anna Borbély EMAIL logo
From the journal Glottotheory

Abstract

The main goal of the present paper is to demonstrate the usefulness of some modern cluster analytic techniques in linguistics by means of data from a longitudinal study of language shift of a Romanian community living in Hungary (Borbély 2016). Based on this sample we attempted to explore statistically valid and linguistically meaningful homogeneous types, which characterize bilingual adult persons of a Romanian community in Hungary. Cluster analyses identified seven main types of language shift. It was also shown that the way from the monolingual Romanian to the monolingual Hungarian state is not a linear process. This may give a chance to a successful intervention to slow down the process of assimilation and language shift. Our analyses demonstrated also the usefulness of the MORI indices (Vargha Bergman & Takács 2016) in determining a proper cluster number, and in the validation of a cluster structure, mainly with the application of a correlated random multidimensional normal data set.

Acknowledgments

The preparation of the present article was supported by the National Research, Development and Innovation Office of Hungary (Grant No. K 116965). It was also written in the framework of “Languag-E-Chance”: Development of language conscious school, bilingual deaf education and innovative methods and tools of knowledge exploitable by language – RIL-HAS Languag-E-Chance Educational Research Group’s project (2016–2020).

References

Aljumily, R. (2016). The Anonymous 1821 Translation of Goethe’s Faustus: A Cluster Analytic Approach. Global Journal of Human-Social Science Research, 15(11).Search in Google Scholar

Bergman, L. R., D. Magnusson & B. M. El-Khouri (2003). Studying individual development in an interindividual context. A Person-oriented approach. Mahwa, New Jersey, London: Lawrence-Erlbaum Associates.10.4324/9781410606822Search in Google Scholar

Borbély, A. (2015). Studying sustainable bilingualism: Comparing the choices of languages in Hungary’s six bilingual national minorities. International Journal of the Sociology of Language, 236, 155–179.10.1515/ijsl-2015-0025Search in Google Scholar

Borbély, A. (2016). Sustainable bilingualism and language shift: Longitudinal research in Romanian–Hungarian bilingual Kétegyháza (Hungary). Acta Linguistica Hungarica, 63 (1), 23–61.10.1556/064.2016.63.1.2Search in Google Scholar

Chen, A. C. H. (2016). A critical evaluation of text difficulty development in ELT textbook series: A corpus-based approach using variability neighbor clustering. System, 58, 64–81.10.1016/j.system.2016.03.011Search in Google Scholar

Desgraupes, B. (2013). Clustering Indices. University Paris Ouest, Lab Modal’X, April 2013. https://cran.r-project.org/web/packages/clusterCrit/vignettes/clusterCrit.pdf. Downloaded: August 28, 2015.Search in Google Scholar

Gal, S. (1979). Language Shift: Social determinants of linguistic change in bilingual Austria. New York: Academic Press.Search in Google Scholar

Houtzagers, P., J. Nerbonne & J. Prokić (2010). Quantitative and traditional classifications of Bulgarian dialects compared. Scando-Slavica 59 (2), 163–188.10.1080/00806765.2010.530801Search in Google Scholar

Jain, A. K. (2010). Data clustering: 50 years beyond K-means. Pattern Recognition Letters, 31, 651–666.10.1016/j.patrec.2009.09.011Search in Google Scholar

Kovatchev, V., M. Salamó & M. A. Martí (2016). Comparing Distributional Semantics Models for identifying groups of semantically related words. Procesamiento del Lenguaje Natural, 57, 109–116.Search in Google Scholar

Moisl, H. (2015). Cluster analysis for corpus linguistics (Vol. 66). de Gruyter.10.1515/9783110363814Search in Google Scholar

Rendón, E., I. Abundez, A. Arizmendi & E. M. Quiroz (2011). Internal versus external cluster validation indexes. International Journal of Computers and Communications, 5 (1), 27–34.Search in Google Scholar

Vargha, A., L. R. Bergman & S. Takács (2016). Performing cluster analysis within a person-oriented context: Some methods for evaluating the quality of cluster solutions. Journal for Person-Oriented Research, 2 (1–2), 78–86.10.17505/jpor.2016.08Search in Google Scholar

Vargha, A., B. Torma & L. R. Bergman (2015). ROPstat: A general statistical package useful for conducting person-oriented analyses. Journal for Person-Oriented Research, 1, (1–2), 87–98.10.17505/jpor.2015.09Search in Google Scholar

Xie, X. L. & G. Beni (1991). A validity measure for fuzzy clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13 (4), 841–846.10.1109/34.85677Search in Google Scholar

Published Online: 2017-12-5
Published in Print: 2017-12-20

© 2017 Walter de Gruyter GmbH, Berlin/Boston

Downloaded on 19.4.2024 from https://www.degruyter.com/document/doi/10.1515/glot-2017-0013/html
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