Beyond cognacy

TL;DR

This paper compares traditional expert-annotated cognate-based phylogenetic methods with two automated approaches, finding that MSA-based inference offers more accurate and scalable language phylogenies.

Contribution

It introduces and evaluates two automated methods for language phylogeny inference, demonstrating MSA-based approach's superior performance over existing methods.

Findings

MSA-based inference produces trees more consistent with linguistic classifications.

MSA approach better predicts typological variation.

Phylogenetic signal is clearer with MSA-based methods.

Abstract

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.