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

    Author(s):
    Gerhard Jäger, Johann-Mattis LIst (see profile) , Taraka Rama, Johannes Wahle
    Date:
    2018
    Group(s):
    Digital Humanists, Linguistics
    Item Type:
    Conference proceeding
    Tag(s):
    computational historical linguistics, phylogenetic reconstruction., phylogenetic reconstruction, evaluation
    Permanent URL:
    http://dx.doi.org/10.17613/j93p-fb19
    Abstract:
    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.
    Metadata:
    xml
    Published as:
    Conference proceeding     Show details
    Pub. DOI:
    10.18653/v1/N18-2063
    Publisher:
    Association for Computational Linguistics
    Pub. Date:
    2018-5-30
    Proceeding:
    Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
    Page Range:
    393 - 400
    Status:
    Published
    Last Updated:
    5 years ago
    License:
    All Rights Reserved
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