# Are Automatic Methods for Cognate Detection Good Enough for Phylogenetic Reconstruction in Historical Linguistics?

arXiv: https://arxiv.org/abs/1804.05416 · HTML: https://mlsanity.com/paper/1804.05416/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Apr 2018.

## Built on by (most cited first, 15 of 15)

- [Automated Cognate Detection as a Supervised Link Prediction Task with Cognate Transformer](https://mlsanity.com/paper/2402.02926/) (2024)
- [Harnessing Cross-lingual Features to Improve Cognate Detection for Low-resource Languages](https://mlsanity.com/paper/2112.08789/) (2021)
- [Automating Sound Change Prediction for Phylogenetic Inference: A Tukanoan Case Study](https://mlsanity.com/paper/2402.01582/) (2024)
- [Cognition-aware Cognate Detection](https://mlsanity.com/paper/2112.08087/) (2021)
- [Jambu: A historical linguistic database for South Asian languages](https://mlsanity.com/paper/2306.02514/) (2023)
- [Are Sounds Sound for Phylogenetic Reconstruction?](https://mlsanity.com/paper/2402.02807/) (2024)
- [Challenge Dataset of Cognates and False Friend Pairs from Indian Languages](https://mlsanity.com/paper/2112.09526/) (2021)
- [Transformed Protoform Reconstruction](https://mlsanity.com/paper/2307.01896/) (2023)
- [A Likelihood Ratio Test of Genetic Relationship among Languages](https://mlsanity.com/paper/2404.00284/) (2024)
- [Utilizing Wordnets for Cognate Detection among Indian Languages](https://mlsanity.com/paper/2112.15124/) (2021)
- [Pairing Orthographically Variant Literary Words to Standard Equivalents Using Neural Edit Distance Models](https://mlsanity.com/paper/2401.15068/) (2024)
- [Beyond cognacy](https://mlsanity.com/paper/2507.03005/) (2025)
- [Neural String Edit Distance](https://mlsanity.com/paper/2104.08388/) (2021)
- [Subword-Based Comparative Linguistics across 242 Languages Using Wikipedia Glottosets](https://mlsanity.com/paper/2601.18791/) (2026)
- [Self-Supervised Lexical Representation Learning for Fast, Large-Scale Phylogenetic Inference](https://mlsanity.com/paper/2609.05262/) (2026)

## Abstract (arXiv)

We evaluate the performance of state-of-the-art algorithms for automatic cognate detection by comparing how useful automatically inferred cognates are for the task of phylogenetic inference compared to classical manually annotated cognate sets. Our findings suggest that phylogenies inferred from automated cognate sets come close to phylogenies inferred from expert-annotated ones, although on average, the latter are still superior. We conclude that future work on phylogenetic reconstruction can profit much from automatic cognate detection. Especially where scholars are merely interested in exploring the bigger picture of a language family's phylogeny, algorithms for automatic cognate detection are a useful complement for current research on language phylogenies.