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Article published In:
Diachronica
Vol. 38:4 (2021) ► pp.506–564

Statistical evidence for the Proto-Indo-European-Euskarian hypothesis

A word-list approach integrating phonotactics

| The Graduate Center, CUNY
| Google (Japan)
Based on a new reconstruction of Proto-Basque, and regular sound correspondences between this Proto-Basque and Proto-Indo-European as standardly reconstructed, Blevins (2018) argues that Proto-Basque and Proto-Indo-European have a common ancestor that pre-dates the two proto-languages. Part of this argument is based on proposed Proto-Indo-European/Proto-Basque cognate sets that include basic vocabulary items. In this study we offer statistical support for Blevins’ conclusions by using a Monte Carlo simulation that allows us to estimate the probability that the proposed lexical correspondences could have arisen by chance. The method makes use of phonotactic language models to generate possible words in a pair of languages, and then attempts to discover consistent correspondences between the words, producing a list of possible “cognates”. The method differs from some previous approaches by considering matches between all segments in the word pairs. By running such a simulation a large number of times, we can estimate the probability that two languages with the given phonotactics could have produced the number of cognate pairs observed in the actual data. The method is independently assessed by comparing wordlists from 100 pairs of languages, related and unrelated, where relations are known. Our conclusion is that the proposed correspondences are unlikely to have arisen by chance, supporting a distant relationship between Proto-Basque as reconstructed by Blevins (2018) and Proto-Indo-European.
Keywords: Proto-Indo-European-Euskarian hypothesis, Proto-Basque, statistical evidence, Monte Carlo simulation, phonotactics, long-distance relationships
Article outline
  • 1.The Proto-Indo-European-Euskarian hypothesis
    • 1.1Eliminating potential loans
    • 1.2Comparing basic vocabulary
  • 2.Cognate sets and semantic lumping
  • 3.Computational methods for lexically based assessments of genetic relatedness
    • 3.1Computing probabilities of chance similarities
    • 3.2Earlier modeling of cognate detection, and the significance of cognates
    • 3.3A synopsis of our proposed method
  • 4.The experiment
    • 4.1Language pairs and word lists
    • 4.2Language model training
    • 4.3Language models
    • 4.4Alignment and potential cognates
    • 4.5Simulations
  • 5.Results
  • 6.Verifying the method with a large list of language pairs
  • 7.Concluding remarks
  • Acknowledgements
  • Notes
  • Abbreviations
  • References
Published online: 6 May 2021
DOI logo
https://doi.org/10.1075/dia.19014.ble
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