Bipartite spectral graph partitioning to co-cluster varieties and sound correspondences in dialectology

Abstract

In this study we used bipartite spectral graph partitioning to simultaneously cluster varieties and sound correspondences in Dutch dialect data. While clustering geographical varieties with respect to their pronunciation is not new, the simultaneous identification of the sound correspondences giving rise to the geographical clustering presents a novel opportunity in dialectometry. Earlier methods aggregated sound differences and clustered on the basis of aggregate differences. The determination of the significant sound correspondences which co-varied with cluster membership was carried out on a post hoc basis. Bipartite spectral graph clustering simultaneously seeks groups of individual sound correspondences which are associated, even while seeking groups of sites which share sound correspondences. We show that the application of this method results in clear and sensible geographical groupings and discuss the concomitant sound correspondences.