Beyond cognacy (2025)

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.

## introduction originally developed in computational biology, quantitative methods for phylogenetic reconstruction using likelihood-based inference frameworks have now gained widespread acceptance in comparative linguistics. this is evident from the growing number of computational phylogenies proposed for some of the world's largest language families, including dravidian @xcite , sino-tibetan @xcite , and indo-european @xcite . moreover, fully automated approaches -where even cognate identification is performed algorithmicallyhave demonstrated a surprising degree of robust-ness @xcite . in contrast to the precomputational era of historical linguistics, where such detailed reconstructions were rare, the generation of fully resolved phylogenies with branch lengths and, in some cases, estimated divergence dates has now become a standard practice in studies of language evolution. despite the increasing recognition of computational language phylogenies as a useful addition to the comparative linguistics toolkit, skepticism remains prevalent. a key concern raised by critics is that phylogenetic analyses are often based on cognate sets-groups of historically related words-extracted from semantically aligned word lists. since these cognate sets are based on expert annotations, they are sparse, labor-intensive to acquire, and raise concerns regarding replicability. another limitation of phylogenetic inference based on cognate classes is that it is by definition constrained to individual language families. there is legitimate interest in automatically inferred trees spanning larger collections of languages, perhaps from the entire world. such trees provide information about the strength of evidence for putative macro-families @xcite . furthermore, they are useful for downstream tasks such as the statistical modeling of global language evolution @xcite . the literature contains several proposed workflows for extracting character matrices from word lists without cognate annotations, which can then used as input for likelihood-based phylogenetic inference. this paper presents a comparison of cognate-based phylogenetic inference with two such proposals, the one by @xcite and the one by @xcite . these methods are evaluated in three ways: (1) by comparing the inferred phylogenies with the glottolog expert classification @xcite , (2) how well the inferred phylogenies fit to the typological features from @xcite , and (3) an estimation of the strength of the phylogenetic signal in the data, which is inferred with the software pypythia @xcite . ## results the evaluation results for the entire dataset are shown in the aggregated evaluation results for the 14 largest language families are shown in the results for the individual families are given in when focusing on phylogenetic inference at the level of individual families, we find a considerable variation between families. this applies both to the numerical evaluation results and the relative ranking of the three methods considered here. the msa method tends to produce the lowest phylogenetic difficulty, while there is no discernible trend regarding the fit to glottolog and to grambank. this picture changes considerably when we focus on datasets covering languages from many different families. here, the msa method consistently outperforms the other two methods. this is particularly evident in the comparison with glottolog, where the msa method yields the lowest gqd values. the msa method also leads to the lowest aic values, indicating a better fit to the grambank typological features. the phylogenetic difficulty is also lowest for the msa method. ## discussion these findings suggest that the msa method is a promising alternative to traditional cognate-based methods. it is competitive with the more laborintensive method based on manual cognate annotations, as well as the method using automatically detected cognate classifications, when considering individual language families. for global datasets, the msa method clearly outperforms the other two methods. this is particularly evident in the comparison with glottolog, where the msa method yields the lowest gqd values. the msa method also tends to produce the lowest aic values, indicating a better fit to the grambank typological features. the phylogenetic difficulty is also lowest for the msa method.