Are Automatic Methods for Cognate Detection Good Enough for Phylogenetic Reconstruction in Historical Linguistics?
This addresses the problem of automating phylogenetic reconstruction in historical linguistics, offering a useful complement for researchers exploring language family phylogenies, though it is incremental as it builds on existing methods.
The study evaluated state-of-the-art algorithms for automatic cognate detection by comparing their usefulness in phylogenetic inference to manually annotated sets, finding that automated methods come close but are still slightly inferior on average.
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.