• 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 cog-
    nate sets come close to phylogenies inferred
    from expert-annotated ones, although on average, the latter are still superior. We con-
    clude 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.