Glottochronology and its application to the Balto-Slavic languages

Novotná, Petra; Blažek, Václav

doi:10.15388/baltistica.42.2.1168

Cited by 6 publications

(5 citation statements)

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“…This result of the classification of the Slavic languages is more straightforward than that achieved by examining the type-token relationship in Slavic languages based on the same Slavic parallel corpus in Kelih's study [17], which in turn only yielded a rank of the 12 Slavic languages reflecting their genetic closeness without suggesting how the languages should be classified. This result is also generally comparable with those achieved by other methods including lexicostatistics [21]. The cluster analysis also involved English and Chinese, which are non-Slavic.…”

Section: Resultssupporting

confidence: 83%

Language clustering with word co-occurrence networks based on parallel texts

Liu

Jin

2013

Chin. Sci. Bull.

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This study investigates the feasibility of applying complex networks to fine-grained language classification and of employing word co-occurrence networks based on parallel texts as a substitute for syntactic dependency networks in complex-network-based language classification. 14 word co-occurrence networks were constructed based on parallel texts of 12 Slavic languages and 2 non-Slavic languages, respectively. With appropriate combinations of major parameters of these networks, cluster analysis was able to distinguish the Slavic languages from the non-Slavic and correctly group the Slavic languages into their respective sub-branches. Moreover, the clustering could also capture the genetic relationships of some of these Slavic languages within their sub-branches. The results have shown that word co-occurrence networks based on parallel texts are applicable to fine-grained language classification and they constitute a more convenient substitute for syntactic dependency networks in complex-networkbased language classification. word co-occurrence network, Slavic languages, parallel texts, language classification, cluster analysis Citation:Liu H T, Cong J. Language clustering with word co-occurrence networks based on parallel texts.

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Section: Resultssupporting

confidence: 83%

Language clustering with word co-occurrence networks based on parallel texts

Liu

Jin

2013

Chin. Sci. Bull.

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“…We applied a lexicostatistical approach to refine the phylogeny of the extant Balto-Slavic languages [ 6 , 7 , 54 ], focusing here particularly on the Slavic sub-branch topology and temporal estimates (for lexicostatistical dataset and methodology see S2 File , Figs A-M in S2 File , Tables A-C in S3 File ; S1 Dataset ). The initial division of Proto-Slavic remains unresolved: a ternary split into West, East and South dated to around 1900 YBP is suggested in the consensus phylogenetic tree ( Fig 1 upper panel, Fig G in S2 File ; see Figs B-F in S2 File for Proto-Slavic split discrepancies between different phylogenetic methods).…”

Section: Resultsmentioning

confidence: 99%

“…The Balto-Slavic node was recognized already in the pioneer Indo-European tree by [ 9 ]. The split between Baltic and Slavic branches has been dated to around 3,500–2,500 YBP [ 6 – 8 ], whereas further diversification of the Slavic languages probably occurred much later, around 1,700–1,300 YBP according to [ 6 – 8 , 10 – 12 ]. The phenomenon of the “Slavicization” of Europe–dispersion of the Slavic languages–was discussed in early studies [ 13 – 15 ].…”

Section: Introductionmentioning

confidence: 99%

Genetic Heritage of the Balto-Slavic Speaking Populations: A Synthesis of Autosomal, Mitochondrial and Y-Chromosomal Data

et al. 2015

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The Slavic branch of the Balto-Slavic sub-family of Indo-European languages underwent rapid divergence as a result of the spatial expansion of its speakers from Central-East Europe, in early medieval times. This expansion–mainly to East Europe and the northern Balkans–resulted in the incorporation of genetic components from numerous autochthonous populations into the Slavic gene pools. Here, we characterize genetic variation in all extant ethnic groups speaking Balto-Slavic languages by analyzing mitochondrial DNA (n = 6,876), Y-chromosomes (n = 6,079) and genome-wide SNP profiles (n = 296), within the context of other European populations. We also reassess the phylogeny of Slavic languages within the Balto-Slavic branch of Indo-European. We find that genetic distances among Balto-Slavic populations, based on autosomal and Y-chromosomal loci, show a high correlation (0.9) both with each other and with geography, but a slightly lower correlation (0.7) with mitochondrial DNA and linguistic affiliation. The data suggest that genetic diversity of the present-day Slavs was predominantly shaped in situ, and we detect two different substrata: ‘central-east European’ for West and East Slavs, and ‘south-east European’ for South Slavs. A pattern of distribution of segments identical by descent between groups of East-West and South Slavs suggests shared ancestry or a modest gene flow between those two groups, which might derive from the historic spread of Slavic people.

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“…(2) Glottochronology-based tree Novotná and Blažek (2007) have used a classical glottochronological approach to generate a genetic a tree for the Balto-Slavic languages. Their approach employs a manual calculation of pairwise distances between languages based on the recognition of cognates.…”

Section: Fine-grained Analysis Of Similaritiesmentioning

confidence: 99%

Rediscovering the Slavic Continuum in Representations Emerging from Neural Models of Spoken Language Identification

Abdullah,

Kudera,

Avgustinova

et al. 2020

Preprint

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Deep neural networks have been employed for various spoken language recognition tasks, including tasks that are multilingual by definition such as spoken language identification. In this paper, we present a neural model for Slavic language identification in speech signals and analyze its emergent representations to investigate whether they reflect objective measures of language relatedness and/or non-linguists' perception of language similarity. While our analysis shows that the language representation space indeed captures language relatedness to a great extent, we find perceptual confusability between languages in our study to be the best predictor of the language representation similarity.

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Glottochronology and its application to the Balto-Slavic languages

Cited by 6 publications

References 0 publications

Language clustering with word co-occurrence networks based on parallel texts

Language clustering with word co-occurrence networks based on parallel texts

Genetic Heritage of the Balto-Slavic Speaking Populations: A Synthesis of Autosomal, Mitochondrial and Y-Chromosomal Data

Rediscovering the Slavic Continuum in Representations Emerging from Neural Models of Spoken Language Identification

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