September 5, 2025Open Research EuropeOpen Access

A systematic exploration of current limitations of cognate-based phylogenetic inference

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Analysis reveals limitations in cognate data application within historical linguistics, highlighting the need for improved datasets.

Cognate-based phylogenetic inference faces challenges with data size and representation methods.
Standard binary representation of cognate data is limiting, while alternative methods show promise but are underexplored.
Evolutionary models designed for cognate data struggle with overparameterization due to insufficient datasets.
Machine learning approaches for cognate data are also limited, emphasizing the need for larger and diverse data collections.

Häuser et al. (2025) studied this question.

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September 5, 2025Open Research EuropeOpen Access

View Full Paper

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Authors

Analysis reveals limitations in cognate data application within historical linguistics, highlighting the need for improved datasets.

Cognate-based phylogenetic inference faces challenges with data size and representation methods.
Standard binary representation of cognate data is limiting, while alternative methods show promise but are underexplored.
Evolutionary models designed for cognate data struggle with overparameterization due to insufficient datasets.
Machine learning approaches for cognate data are also limited, emphasizing the need for larger and diverse data collections.

Häuser et al. (2025) studied this question.

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