German Dialectometry

Schiltz, Guillaume

doi:10.1007/978-3-642-80098-6_45

Cited by 3 publications

(2 citation statements)

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“…Haag (1898) (discussed by Schiltz (1996)) proposed a quantitative technique in which the darkness of a border between two adjacent sites was reflected by the number of differences counted in a given sample, and similar maps have been in use since. This appears to be the first published proposal of how one might operationalize the idea of ‘bundling isoglosses’, and it clearly implies aggregating over a variety of features, so it is an important early recognition of the need for aggregation.…”

Section: Previous Aggregate Viewsmentioning

confidence: 99%

Data‐Driven Dialectology

Nerbonne

2009

Language and Linguist. Compass

117

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Most studies of language variation proceed from the geographic or social distribution of single elements (features), and find it difficult to proceed further. Data‐driven dialectology, and more generally, data‐driven variationist studies, begin instead from an aggregate view of language variation and reap immediate benefits in dealing with well‐known exceptions in the distributions of single features and in avoiding the need to select which features to use as the basis of characterizations. But the major advance is the opportunity to characterize general tendencies in linguistic variation.

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Section: Previous Aggregate Viewsmentioning

confidence: 99%

Data‐Driven Dialectology

Nerbonne

2009

Language and Linguist. Compass

117

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“…Dialectometry is still a new ®eld in dialectology, originating in the transfer of taxonomic procedures to dialect geography (Schiltz, 1995). In dialectometry similarity functions are used to put all pairs of locations in relation to one another.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Dialect Features

Wilhelm

Sander

1998

J Royal Statistical Soc D

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Projecting Dialect Distances to Geography: Bootstrap Clustering vs. Noisy Clustering

Nerbonne

Heeringa

Manni

et al. 2008

Data Analysis, Machine Learning and Applications

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Abstract. Dialectometry produces aggregate distance matrices in which a distance is specified for each pair of sites. By projecting groups obtained by clustering onto geography one compares results with traditional dialectology, which produced maps partitioned into implicitly non-overlapping dialect areas. The importance of dialect areas has been challenged by proponents of continua, but they too need to compare their findings to older literature, expressed in terms of areas.Simple clustering is unstable, meaning that small differences in the input matrix can lead to large differences in results (Jain et al. 1999). This is illustrated with a 500-site data set from Bulgaria, where input matrices which correlate very highly (r = 0.97) still yield very different clusterings. Kleiweg et al. (2004) introduce composite clustering, in which random noise is added to matrices during repeated clustering. The resulting borders are then projected onto the map.The present contribution compares Kleiweg et al.'s procedure to resampled bootstrapping, and also shows how the same procedure used to project borders from composite clustering may be used to project borders from bootstrapping.

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German Dialectometry

Cited by 3 publications

References 0 publications

Data‐Driven Dialectology

Data‐Driven Dialectology

Analysis of Dialect Features

Projecting Dialect Distances to Geography: Bootstrap Clustering vs. Noisy Clustering

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