# Global-scale phylogenetic linguistic inference from lexical resources

arXiv: https://arxiv.org/abs/1802.06079 · HTML: https://mlsanity.com/paper/1802.06079/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Feb 2018.

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

- [Analyzing the Effect of Linguistic Similarity on Cross-Lingual Transfer: Tasks and Experimental Setups Matter](https://mlsanity.com/paper/2501.14491/) (2025)
- [Phylogenetic typology](https://mlsanity.com/paper/2103.10198/) (2021)
- [A Likelihood Ratio Test of Genetic Relationship among Languages](https://mlsanity.com/paper/2404.00284/) (2024)
- [Computational Approaches for Integrating out Subjectivity in Cognate Synonym Selection](https://mlsanity.com/paper/2404.19328/) (2024)
- [Exploring language relations through syntactic distances and geographic proximity](https://mlsanity.com/paper/2403.18430/) (2024)
- [Computational Typology](https://mlsanity.com/paper/2504.15642/) (2025)
- [Beyond cognacy](https://mlsanity.com/paper/2507.03005/) (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)
- [Still no evidence for an effect of the proportion of non-native speakers on language complexity -- A response to Kauhanen, Einhaus & Walkden (2023)](https://mlsanity.com/paper/2305.00217/) (2023)
- [Patterns of Persistence and Diffusibility across the World's Languages](https://mlsanity.com/paper/2401.01698/) (2024)
- [The Cognate Data Bottleneck in Language Phylogenetics](https://mlsanity.com/paper/2507.00911/) (2025)
- [Subword-Based Comparative Linguistics across 242 Languages Using Wikipedia Glottosets](https://mlsanity.com/paper/2601.18791/) (2026)
- [Universal Conceptual Structure in Neural Translation: Probing NLLB-200's Multilingual Geometry](https://mlsanity.com/paper/2603.02258/) (2026)
- [Phonological Fossils: Machine Learning Detection of Non-Mainstream Vocabulary in Sulawesi Basic Lexicon](https://mlsanity.com/paper/2604.00023/) (2026)
- [Self-Supervised Lexical Representation Learning for Fast, Large-Scale Phylogenetic Inference](https://mlsanity.com/paper/2609.05262/) (2026)

## Abstract (arXiv)

Automatic phylogenetic inference plays an increasingly important role in computational historical linguistics. Most pertinent work is currently based on expert cognate judgments. This limits the scope of this approach to a small number of well-studied language families. We used machine learning techniques to compile data suitable for phylogenetic inference from the ASJP database, a collection of almost 7,000 phonetically transcribed word lists over 40 concepts, covering two third of the extant world-wide linguistic diversity. First, we estimated Pointwise Mutual Information scores between sound classes using weighted sequence alignment and general-purpose optimization. From this we computed a dissimilarity matrix over all ASJP word lists. This matrix is suitable for distance-based phylogenetic inference. Second, we applied cognate clustering to the ASJP data, using supervised training of an SVM classifier on expert cognacy judgments. Third, we defined two types of binary characters, based on automatically inferred cognate classes and on sound-class occurrences. Several tests are reported demonstrating the suitability of these characters for character-based phylogenetic inference.