Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-cd9895bd7-hc48f Total loading time: 0 Render date: 2024-12-26T04:19:55.266Z Has data issue: false hasContentIssue false

9 - Comparing Methods for the Evaluation of Cluster Structures in Multidimensional Analyses

Concessive Constructions in Varieties of English

from Part III - Perspectives on Multifactorial Methods

Published online by Cambridge University Press:  06 May 2022

By
Ole Schützler
Edited by
Ole Schützler  and
Julia Schlüter
Ole Schützler
Affiliation:
Universität Leipzig
Julia Schlüter
Affiliation:
Universität Bamberg
Get access

Summary

This chapter sets out by discussing the way in which multidimensional techniques and visualizations have been used to analyse linguistic data. While, for instance, multidimensional scaling and unrooted phenograms (or NeighborNets) have primarily been designed for exploratory purposes, the author argues that they are in fact regularly used to put linguistic assumptions or hypotheses to the test. Cluster goodness (in terms of internal coherence and external distance from other clusters) in such approaches are typically evaluated based on a two-dimensional visualization. The author compares the affordances and limitations of visual inspection with a quantitative set of metrics that directly relates to visual displays but adds a degree of precision not attained by the human eye. The empirical part of the paper applies both approaches to a study of concessive constructions in six varieties of English, based on spoken and written material from the International Corpus of English. The author suggests that the new metrics can be usefully applied to a variety of multidimensional techniques to endow them with a measure of objectivity.

Keywords

multidimensional datadistanceMDSNeighborNetclusterInternational Corpus of Englishdistance matrixdendrogram
Type
Chapter
Information
Data and Methods in Corpus Linguistics
Comparative Approaches
 , pp. 259 - 288
Publisher: Cambridge University Press
Print publication year: 2022

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Further Reading

Borg, Ingwer, and Patrick, J. F. Groenen. 2005. Modern Multidimensional Scaling. New York: Springer.Google Scholar
Everitt, Brian S., Landau, Sabine, Leese, Morven and Stahl, Daniel. 2011. Cluster Analysis. Chichester, UK: John Wiley & Sons.CrossRefGoogle Scholar
Moisl, Hermann. 2015. Cluster Analysis for Corpus Linguistics. Berlin: Mouton de Gruyter.Google Scholar

References

Anscombre, Jean-Claude. 1989. Théorie de l’argumentation, topoï, et structuration discursive. Revue Québécoise de Linguistique 18(1). 13–55.Google Scholar
Anthony, Laurence. 2018. AntConc. A Freeware Corpus Analysis Toolkit for Concordancing and Text Analysis. v. 3.5.7. www.laurenceanthony.net/software.html.Google Scholar
Azar, Moshe. 1997. Concessive Relations as Argumentations. Text 17(3). 301–16.Google Scholar
Bengtsson, Henrik, Bravo, Hector Corrada, Gentleman, Robert et al. 2019. Functions that Apply to Rows and Columns of Matrices (and to Vectors) (‘matrixStats’). R package. v. 0.55.0. https://cran.r-project.org/web/packages/matrixStats/matrixStats.pdf.Google Scholar
Biber, Douglas. 1988. Variation across Speech and Writing. Cambridge: Cambridge University Press.Google Scholar
Borg, Ingwer, and Groenen, Patrick J. F.. 2005. Modern Multidimensional Scaling. New York: Springer.Google Scholar
Bürkner, Paul. 2019. Bayesian Regression Models Using ‘Stan’ (‘brms’). R package v. 2.8.0. https://cran.r-project.org/web/packages/brms/brms.pdf.Google Scholar
Cormack, R. M. 1971. A Review of Classification. Journal of the Royal Statistical Society A 134. 321–67.Google Scholar
Di Meola, Claudio. 1998. Zur Definition einer logisch-semantischen Kategorie: Konzessivität als „versteckte Kausalität“. Linguistische Berichte 175. 329–52.Google Scholar
Diessel, Holger. 2005. Competing Motivations for the Ordering of Main and Adverbial Clauses. Linguistics 43(3). 449–70.Google Scholar
Dugard, Pat, Todman, John and Staines, Harry. 2010. Approaching Multivariate Analysis: A Practical Introduction. New York: Routledge.Google Scholar
Dunn, J. C. 1974. Well Separated Clusters and Optimal Fuzzy Partitions. Journal of Cybernetics 4(1). 95–104.Google Scholar
Everitt, Brian S., Landau, Sabine, Leese, Morven and Stahl, Daniel. 2011. Cluster Analysis. Chichester, UK: John Wiley & Sons.Google Scholar
Gordon, Allan D. 1999. Classification. Boca Raton, FL: Chapman and Hall/CRC.Google Scholar
Gries, Stefan Th. 2013. Statistics for Linguistics with R: A Practical Introduction. Berlin: Mouton de Gruyter.Google Scholar
Handl, Julia, Knowles, Joshua and Kell, Douglas B.. 2005. Computational Cluster Validation in Post-Genomic Data Analysis. Bioinformatics 21(15). 3201–212.Google Scholar
Hawkins, John A. 1994. A Performance Theory of Order and Constituency. Cambridge: Cambridge University Press.Google Scholar
Hermodsson, Lars. 1994. Der Begriff „konzessiv“: Terminologie und Analysen. Studia Neophilologica 66. 59–75.Google Scholar
Hilpert, Martin. 2011. Dynamic Visualizations of Language Change: Motion Charts on the Basis of Bivariate and Multivariate Data from Diachronic Corpora. International Journal of Corpus Linguistics 4(16). 435–61.Google Scholar
Hilpert, Martin. 2013. Constructional Change in English: Developments in Allomorphy, Word Formation, and Syntax. Cambridge: Cambridge University Press.Google Scholar
Huddleston, Rodney D. and Pullum, Geoffrey K.. 2002. The Cambridge Grammar of the English Language. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Huson, Daniel H. and Bryant, David. 2017. SplitsTree4. Software for Computing Phylogenetic Networks. v. 4.16.6. www.splitstree.org/.Google Scholar
Kapp, Amy V. and Tibshirani, Robert. 2007. Are Clusters Found in One Dataset Present in Another Dataset? Biostatistics 8(1). 9–31.CrossRefGoogle ScholarPubMed
König, Ekkehard. 2006. Concessive Clauses. In Brown, Keith, ed. Encyclopedia of Language and Linguistics. Vol. 2. Amsterdam: Elsevier. 820–4.Google Scholar
Kortmann, Bernd, and Wolk, Christoph. 2012. Morphosyntactic Variation in the Anglophone World: A Global Perspective. In Kortmann, Bernd & Lunkenheimer, Kerstin, eds. The Mouton World Atlas of Variation in English. Berlin: Mouton de Gruyter. 906–36.Google Scholar
Kortmann, Bernd, and Lunkenheimer, Kerstin, eds. 2012. The Mouton World Atlas of Variation in English. Berlin: Mouton de Gruyter.CrossRefGoogle Scholar
Kruskal, Joseph B., and Wish, Myron. 1978. Multidimensional Scaling. Beverly Hills, CA/London: Sage Publications.Google Scholar
Moisl, Hermann. 2015. Cluster Analysis for Corpus Linguistics. Berlin: Mouton de Gruyter.Google Scholar
Nelson, Gerald. 1996. The Design of the Corpus. In Greenbaum, Sidney, ed. Comparing English Worldwide: The International Corpus of English. Oxford: Clarendon Press. 27–35.Google Scholar
Quirk, Randolph, Greenbaum, Sidney, Leech, Geoffrey and Svartvik, Jan. 1985. A Comprehensive Grammar of the English Language. London: Arnold.Google Scholar
R Development Core Team. 2018. R: A Language and Environment for Statistical Computing. Version 3.5.2. www.R-project.org.Google Scholar
RStudio Team. 2009–18. RStudio: Integrated Development for R. Version 1.1.453. Boston, MA: RStudio, Inc. www.rstudio.com.Google Scholar
Sarkar, Deepayan, and Andrews, Felix. 2016. Extra Graphical Utilities Based on Lattice (‘lattice extra’). R package, version 0.6–28. https://cran.r-project.org/web/packages/latticeExtra/latticeExtra.pdf.Google Scholar
Sarkar, Deepayan. 2008. Lattice. Multivariate Data Visualization with R. Berlin: Springer.Google Scholar
Sarkar, Deepayan. 2014. Lattice Graphics (‘lattice’). R package, version 0.20–29. http://cran.r-project.org/web/packages/lattice/lattice.pdf.Google Scholar
Schliep, Klaus. 2018. Phylogenetic Reconstruction and Analysis (‘phangorn’). R package version 2.4.0. https://cran.r-project.org/web/packages/phangorn/phangorn.pdf.Google Scholar
Schützler, Ole. 2017. A Corpus-Based Study of Concessive Conjunctions in Three L1-Varieties of English. In Buchstaller, Isabelle and Siebenhaar, Beat, eds. Language Variation – European Perspectives VI: Selected Papers from the Eighth International Conference on Language Variation in Europe (ICLaVE 8), Leipzig, May 2015. Amsterdam/Philadelphia: John Benjamins. 173–84.Google Scholar
Schützler, Ole. 2018. Concessive Conjunctions in Written American English: Diachronic and Genre-Related Changes in Frequency and Semantics. In Whitt, Richard J., ed. Diachronic Corpora, Genre, and Language Change. Amsterdam/Philadelphia: John Benjamins.Google Scholar
Sweetser, Eve E. 1990. From Etymology to Pragmatics: Metaphorical and Cultural Aspects of Semantic Structure. Cambridge: Cambridge University Press.Google Scholar
Szmrecsanyi, Benedikt. 2012. Typological Profile: L1 Varieties. In Kortmann, Bernd, and Lunkenheimer, Kerstin, eds. The Mouton World Atlas of Variation in English. Berlin: Mouton de Gruyter. 826–42.Google Scholar
Werner, Valentin. 2014. The Present Perfect in World Englishes: Charting Unity and Diversity. Bamberg: University of Bamberg Press.Google Scholar
Wheeler, Eric S. 2005. Multidimensional Scaling for Linguistics. In Altmann, Gabriel, Piotrowski, Rajmund G. and Köhler, Reinhard, eds. Quantitative Linguistics: An International Handbook. Berlin: Walter de Gruyter. 548–53.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×