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Computing and Language VariationInternational Journal of Humanities and Arts Computing Volume 2$
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John Nerbonne and Charlotte Gooskens

Print publication date: 2009

Print ISBN-13: 9780748640300

Published to Edinburgh Scholarship Online: September 2012

DOI: 10.3366/edinburgh/9780748640300.001.0001

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Recognising Groups among Dialects

Recognising Groups among Dialects

Chapter:
(p.153) Recognising Groups among Dialects
Source:
Computing and Language Variation
Author(s):

Jelena ProkiĆ

John Nerbonne

Publisher:
Edinburgh University Press
DOI:10.3366/edinburgh/9780748640300.003.0009

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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