Data‐Driven Dialectology

Nerbonne, John

doi:10.1111/j.1749-818x.2008.00114.x

Cited by 117 publications

(67 citation statements)

References 27 publications

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“…We do not have the time or space to review all of the background or range of techniques here, so the presentation will be sketchy. Fortunately, there are good introductions available (Goebl 1984;Heeringa 2004;Goebl 2006;Nerbonne 2009;Nerbonne & Heeringa 2009). …”

Section: Aggregate (Dialectometric) Variationmentioning

confidence: 99%

Measuring the diffusion of linguistic change

Nerbonne

2010

Phil. Trans. R. Soc. B

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We examine situations in which linguistic changes have probably been propagated via normal contact as opposed to via conquest, recent settlement and large-scale migration. We proceed then from two simplifying assumptions: first, that all linguistic variation is the result of either diffusion or independent innovation, and, second, that we may operationalize social contact as geographical distance. It is clear that both of these assumptions are imperfect, but they allow us to examine diffusion via the distribution of linguistic variation as a function of geographical distance. Several studies in quantitative linguistics have examined this relation, starting with Séguy (Séguy 1971 Rev. Linguist. Romane 35,, and virtually all report a sublinear growth in aggregate linguistic variation as a function of geographical distance. The literature from dialectology and historical linguistics has mostly traced the diffusion of individual features, however, so that it is sensible to ask what sort of dynamic in the diffusion of individual features is compatible with Séguy's curve. We examine some simulations of diffusion in an effort to shed light on this question.

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Section: Aggregate (Dialectometric) Variationmentioning

confidence: 99%

Measuring the diffusion of linguistic change

Nerbonne

2010

Phil. Trans. R. Soc. B

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“…For instance, Nerbonne (2009) applied Levenshtein distance for the classification of Dutch and German dialects. Nerbonne finds that the classification offered by Levenshtein distance largely agrees with the traditional dialectological knowledge of Dutch and German areas.…”

Section: Introductionmentioning

confidence: 99%

Computational analysis of Gondi dialects

Rama¹,

Çöltekin²,

Sofroniev³

2017

Proceedings of the Fourth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial)

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“…Second, the techniques AGGREGATE over these linguistic differences, in order, third, to seek the natural groups in the data via clustering or multidimensional scaling (MDS) [6].…”

Section: Introductionmentioning

confidence: 99%

Bipartite spectral graph partitioning for clustering dialect varieties and detecting their linguistic features

Wieling

Nerbonne

2011

Computer Speech & Language

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In this study we use bipartite spectral graph partitioning to simultaneously cluster varieties and identify their most distinctive linguistic features in Dutch dialect data.While clustering geographical varieties with respect to their features, e.g. pronunciation, is not new, the simultaneous identification of the features which give rise to the geographical clustering presents novel opportunities in dialectometry. Earlier methods aggregated sound differences and clustered on the basis of aggregate differences. The determination of the significant features which co-vary with cluster membership was carried out on a post hoc basis. Bipartite spectral graph clustering simultaneously seeks groups of individual features which are strongly associated, even while seeking groups of sites which share subsets of these same features. We show that the application of this method results in clear and sensible geographical groupings and discuss and analyze the importance of the concomitant features.Key words: Bipartite spectral graph partitioning, Clustering, Sound correspondences, Dialectometry, Dialectology, Language variation $ This paper is an extended version of the study 'Bipartite spectral graph partitioning to co-cluster varieties and sound correspondences in dialectology' by Martijn Wieling and John Nerbonne [1]

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Data‐Driven Dialectology

Cited by 117 publications

References 27 publications

Measuring the diffusion of linguistic change

Measuring the diffusion of linguistic change

Computational analysis of Gondi dialects

Bipartite spectral graph partitioning for clustering dialect varieties and detecting their linguistic features

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