Skip Navigation


Literary and Linguistic Computing Advance Access originally published online on June 17, 2005
Literary and Linguistic Computing 2005 20(Suppl 1):125-146; doi:10.1093/llc/fqi026
This Article
Right arrow Full Text (PDF)
Right arrow All Versions of this Article:
20/Suppl/125    most recent
fqi026v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Moisl, H.
Right arrow Articles by Jones, V.
Right arrow Search for Related Content
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author 2005. Published by Oxford University Press on behalf of ALLC and ACH. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Cluster Analysis of the Newcastle Electronic Corpus of Tyneside English: A Comparison of Methods

Hermann Moisl

University of Newcastle, UK

Val Jones

University of Twente, The Netherlands

Correspondence: Hermann Moisl, School of English Literature, Language, and Linguistics, University of Newcastle, Newcastle upon Tyne NE1 7RU, UK E-mail: hermann.moisl{at}ncl.ac.uk
This article examines the feasibility of an empirical approach to sociolinguistic analysis of the Newcastle Electronic Corpus of Tyneside English using exploratory multivariate methods. It addresses a known problem with one class of such methods, hierarchical cluster analysis—that different clustering algorithms can yield different analyses of the same data set, and that there is no obvious way of selecting the best one. The proposed solution is to analyze the data using hierarchical methods in conjunction with one or more fundamentally different types of clustering method, and then to select the analysis on which the hierarchical and the other method(s) agree most closely. A dimensionality reduction method, the self-organizing map (SOM), is used to exemplify this approach. The result is a close though not perfect match between the SOM and complete-link hierarchical analyses, but there is an important reservation—the SOM results vary with changes in user-defined training parameters, and are consequently also open to the criticism of inconsistency. The SOM cannot therefore be an objective arbiter for hierarchical clustering, but the analysis on which they agree gives a better basis for understanding the structure of the data than either method can provide on its own.


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
 Lit Linguist ComputingHome page
S. Hyvonen, A. Leino, and M. Salmenkivi
Multivariate Analysis of Finnish Dialect Data An Overview of Lexical Variation
Lit Linguist Computing, September 1, 2007; 22(3): 271 - 290.
[Abstract] [Full Text] [PDF]


Disclaimer:
Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.