Phonetic Variation in the Traditional English Dialects

Abstract

This study explores the linguistic application of bipartite spectral graph partitioning, a graphtheoretic technique that simultaneously identifies clusters of similar localities as well as clusters of features characteristic of those localities. We compare the results using this approach to previously published results on the same dataset using cluster and principal component analysis (Shackleton, 2007). While the results of the spectral partitioning method and Shackleton's approach overlap to a broad extent, the analyses offer complementary insights into the data. The traditional cluster analysis detects some clusters which are not identified by the spectral partitioning analysis, while the reverse also occurs. Similarly, the principal component analysis and the spectral partitioning analysis detect many overlapping, but also some different linguistic variants. The main benefit of the bipartite spectral graph partitioning method over the alternative approaches remains its ability to simultaneously identify sensible geographical clusters of localities with their corresponding linguistic features.

Language Boundaries Driven by Surface Tension

“…A typical clustering approach [10,12] is to construct a data set giving the frequencies of a wide range of variant pronunciations at different locations, and then to cluster these locations according to the similarity of their aggregated sets of characteristics. Resampling techniques such as bootstrap [69] may be used to generate "fictitious" data sets and improve stability.…”

“…The linearity of our adapted OJK equation (10) ensures that mðr; tÞ remains Gaussian for all time [20] (to see this, note that derivatives are limits of sums, and sums of Gaussian random variables are themselves Gaussian …”

A comparison of statistical methods for the aggregation of regional linguistic variation

“…While the multidimensional scaling identifies continuous patterns of aggregated regional linguistic variation, a cluster analysis can be used to produce a discrete classification of the locations, which can then be mapped in order to identify absolute patterns of aggregated regional linguistic variation. In this analysis, the linguistic distance matrix was subjected to a hierarchical cluster analysis (Shackleton, 2005(Shackleton, , 2007Goebl, 2007;Prokic & Nerbonne, 2008;Wieling & Nerbonne, 2010). A hierarchical cluster analysis identifies clusters of similar objects in a distance matrix by initially assigning each observation to its own cluster and by then repeatedly combining the two most similar clusters to form larger and larger clusters until all of the objects have been combined to form one large cluster.…”

“…Séguy initiated the field of dialectometry to overcome the limitations of the isogloss method, but the analysis of individual linguistic variables and the identification of subsets of variables that exhibit similar patterns are still worthwhile. Most dialectometry analyses, however, focus on the linguistic distance matrix from which information about the patterns exhibited by the individual linguistic variables cannot be extracted directly (although for dialectometry research that addresses some of these issues see Shackleton, 2005Shackleton, , 2007Nerbonne, 2006;Rumpf et al, 2009Rumpf et al, , 2010Wieling and Nerbonne, 2011). These limitations with the standard approach to dialectometry led to the development of an alternative statistical approach to the analysis of regional linguistic variation known as a multivariate spatial analysis (Grieve, 2009;Grieve et al, forthcoming).…”

“…This application of factor analysis is similar to the use of multidimensional scaling in dialectometry, except that because the factor analysis is based on a correlation matrix, as opposed to a distance matrix, it is possible to determine the variables that define each dimension. While factor analysis and the related technique known as principal component analysis are not as common as multidimensional scaling in dialectometry, these statistics have been used on occasion (Shackleton, 2005(Shackleton, , 2007Nerbonne, 2006;Szmrecsanyi and Wolk, forthcoming), as well as in the Atlas of North American English (Labov et al, 2006), although the full power of these techniques is not exploited in any of these studies, nor are any of these analyses based on smoothed variables.…”