Recognising Groups among Dialects
Recognising Groups among Dialects
Dialectometry is a multidisciplinary field that uses various quantitative methods in the analysis of dialect data. Very often those techniques include classification algorithms such as hierarchical clustering algorithms used to detect groups within certain dialect area. Although known for their instability, clustering algorithms are often applied without evaluation or with only partial evaluation. Very small differences in the input data can produce substantially different grouping of dialects. This chapter evaluates algorithms used to detect groups among language dialect varieties measured at the aggregate level. The data used in this research is dialect pronunciation data that consists of various pronunciations of 156 words collected all over Bulgaria. The distances between words are calculated using Levenshtein algorithm, which also resulted in the calculation of the distances between each two sites in the data set. Seven hierarchical clustering algorithms, as well as the k-means and neighbor-joining algorithm, are applied to the calculated distances.
Keywords: dialectometry, classification algorithms, hierarchical clustering algorithms, dialects, pronunciations, Bulgaria, Levenshtein algorithm, k-means, neighbor-joining algorithm
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