A Quantitative categorization of phonemic dialect features in context (read abstract) [Draft of conference poster]

by Naomi Nagy, Xiaoli Zhang, George Nagy, and Edgar W. Schneider. 2005

Poster presented at:

CONTEXT '05: The Fifth International and Interdisciplinary Conference on Modeling and Using Context. Paris, July 2005

and paper published as:

Nagy, Naomi, Xiaoli Zhang, George Nagy & E.W. Schneider. 2005. A Quantitative categorization of phonemic dialect features in context . In A. Dey et al. (eds.) CONTEXT 2005. Lecture Notes in Artificial Intelligence 3554. Springer-Verlag. 326-338.

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Online addenda to the conference presentation and paper

• Appendix: List of abbreviations of language varieties (Download Appendix as .doc file )

• An illustrative example of the representation of linguistic varieties organized in a dissimilarity matrix (Table 2) and grouped into clusters by dendrogram (Table 2 and Fig. 1) (Download Table 2 and Figure 1)

Abstract

We test a method of clustering dialects of English according to patterns of shared phonological features. Previous linguistic research has generally considered phonological features as independent of each other, but context is important: rather than considering each phonological feature individually, we compare the patterns of shared features, or Mutual Information (MI). The dependence of one phonological feature on the others is quantified and exploited. The results of this method of categorizing 59 dialect varieties by 168 binary internal (pronunciation) features are compared to traditional groupings based on external features (e.g., ethnic, geographic). The Mutual Information and size of the groups are calculated for taxonomies at various levels of granularity and these groups are compared to other analyses of geographic and ethnic distribution. Applications that could be improved by using MI methods are suggested.

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