mlsanityData to 7 Oct 2026

Phylogenetic typology

arXiv 2103.10198 ↗First posted 18 Mar 2021q-bio.PE cs.CL q-bio.QMcited by 3

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Abstract (arXiv)

In this article we propose a novel method to estimate the frequency distribution of linguistic variables while controlling for statistical non-independence due to shared ancestry. Unlike previous approaches, our technique uses all available data, from language families large and small as well as from isolates, while controlling for different degrees of relatedness on a continuous scale estimated from the data. Our approach involves three steps: First, distributions of phylogenies are inferred from lexical data. Second, these phylogenies are used as part of a statistical model to statistically estimate transition rates between parameter states. Finally, the long-term equilibrium of the resulting Markov process is computed. As a case study, we investigate a series of potential word-order correlations across the languages of the world.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 3 of its 49 references that are in the map. First posted on arXiv 18 Mar 2021.

Builds on

3 of 49 references.

References · 3

  1. 2015Practical Bayesian model evaluation using leave-one-out cross-validation and WAICcited by 110
  2. 2018Global-scale phylogenetic linguistic inference from lexical resourcescited by 15
  3. 2013The risks of mixing dependency lengths from sequences of different lengthcited by 7

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Citing papers per year: 2022: 1; 2023: 0; 2024: 0; 2025: 2.

3 papers.