# Phylogenetic typology

arXiv: https://arxiv.org/abs/2103.10198 · HTML: https://mlsanity.com/paper/2103.10198/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 49 references that are in the map.
- First posted on arXiv 18 Mar 2021.

## Builds on (most cited first, 3 of 3)

- [Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC](https://mlsanity.com/paper/1507.04544/) (2015)
- [Global-scale phylogenetic linguistic inference from lexical resources](https://mlsanity.com/paper/1802.06079/) (2018)
- [The risks of mixing dependency lengths from sequences of different length](https://mlsanity.com/paper/1304.3841/) (2013)

## Built on by (most cited first, 3 of 3)

- [Word length predicts word order: "Min-max"-ing drives language evolution](https://mlsanity.com/paper/2505.13913/) (2025)
- [Simple Additions, Substantial Gains: Expanding Scripts, Languages, and Lineage Coverage in URIEL+](https://mlsanity.com/paper/2510.27183/) (2025)
- [Challenges of sampling and how phylogenetic comparative methods help: With a case study of the Pama-Nyungan laminal contrast](https://mlsanity.com/paper/2201.00195/) (2022)

## 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.