Phylogenetic typology
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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
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Citing papers per year: 2022: 1; 2023: 0; 2024: 0; 2025: 2.
3 papers.
- 2025Word length predicts word order: "Min-max"-ing drives language evolutioncited by 2
- 2025Simple Additions, Substantial Gains: Expanding Scripts, Languages, and Lineage Coverage in URIEL+cited by 2
- 2022Challenges of sampling and how phylogenetic comparative methods help: With a case study of the Pama-Nyungan laminal contrastcited by 0
- 2025Simple Additions, Substantial Gains: Expanding Scripts, Languages, and Lineage Coverage in URIEL+31 Oct 2025
- 2025Word length predicts word order: "Min-max"-ing drives language evolution20 May 2025
- 2022Challenges of sampling and how phylogenetic comparative methods help: With a case study of the Pama-Nyungan laminal contrast1 Jan 2022