# Beyond cognacy

arXiv: https://arxiv.org/abs/2507.03005 · HTML: https://mlsanity.com/paper/2507.03005/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 24 references that are in the map.
- First posted on arXiv 2 Jul 2025.

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

- [Global-scale phylogenetic linguistic inference from lexical resources](https://mlsanity.com/paper/1802.06079/) (2018)
- [Are Automatic Methods for Cognate Detection Good Enough for Phylogenetic Reconstruction in Historical Linguistics?](https://mlsanity.com/paper/1804.05416/) (2018)
- [A Likelihood Ratio Test of Genetic Relationship among Languages](https://mlsanity.com/paper/2404.00284/) (2024)

## Built on by (most cited first, 1 of 1)

- [Self-Supervised Lexical Representation Learning for Fast, Large-Scale Phylogenetic Inference](https://mlsanity.com/paper/2609.05262/) (2026)

## Abstract (arXiv)

Computational phylogenetics has become an established tool in historical linguistics, with many language families now analyzed using likelihood-based inference. However, standard approaches rely on expert-annotated cognate sets, which are sparse, labor-intensive to produce, and limited to individual language families. This paper explores alternatives by comparing the established method to two fully automated methods that extract phylogenetic signal directly from lexical data. One uses automatic cognate clustering with unigram/concept features; the other applies multiple sequence alignment (MSA) derived from a pair-hidden Markov model. Both are evaluated against expert classifications from Glottolog and typological data from Grambank. Also, the intrinsic strengths of the phylogenetic signal in the characters are compared. Results show that MSA-based inference yields trees more consistent with linguistic classifications, better predicts typological variation, and provides a clearer phylogenetic signal, suggesting it as a promising, scalable alternative to traditional cognate-based methods. This opens new avenues for global-scale language phylogenies beyond expert annotation bottlenecks.