Information retrieval using a singular value decomposition model of latent semantic structure
SIGIR '88 Proceedings of the 11th annual international ACM SIGIR conference on Research and development in information retrieval
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Co-clustering documents and words using bipartite spectral graph partitioning
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Multiple sequence alignments in linguistics
LaTeCH-SHELT&R '09 Proceedings of the EACL 2009 Workshop on Language Technology and Resources for Cultural Heritage, Social Sciences, Humanities, and Education
Evaluating the pairwise string alignment of pronunciations
LaTeCH-SHELT&R '09 Proceedings of the EACL 2009 Workshop on Language Technology and Resources for Cultural Heritage, Social Sciences, Humanities, and Education
TextGraphs-4 Proceedings of the 2009 Workshop on Graph-based Methods for Natural Language Processing
Computer Speech and Language
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In this study we apply hierarchical spectral partitioning of bipartite graphs to a Dutch dialect dataset to cluster dialect varieties and determine the concomitant sound correspondences. An important advantage of this clustering method over other dialectometric methods is that the linguistic basis is simultaneously determined, bridging the gap between traditional and quantitative dialectology. Besides showing that the results of the hierarchical clustering improve over the flat spectral clustering method used in an earlier study (Wieling and Nerbonne, 2009), the values of the second singular vector used to generate the two-way clustering can be used to identify the most important sound correspondences for each cluster. This is an important advantage of the hierarchical method as it obviates the need for external methods to determine the most important sound correspondences for a geographical cluster.