Global-scale phylogenetic linguistic inference from lexical resources

  title={Global-scale phylogenetic linguistic inference from lexical resources},
  author={G. J{\"a}ger},
  journal={Scientific Data},
  • G. Jäger
  • Published 2018
  • Computer Science, Biology, Medicine
  • Scientific Data
  • 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 thirds of the extant… CONTINUE READING
    CogNet: A Large-Scale Cognate Database
    • 3
    • Open Access
    A test of Generalized Bayesian dating: A new linguistic dating method
    • 1
    • Highly Influenced
    • Open Access
    The evolution of daily food sharing: A Bayesian phylogenetic analysis
    • 4
    • Open Access
    2 Hilbert and Hilpert Problems
    • 2019
    Model evaluation in computational historical linguistics


    Publications referenced by this paper.
    On the Accuracy of Language Trees
    • 37
    • Highly Influential
    • Open Access
    Phylogenetic Inference from Word Lists Using Weighted Alignment with Empirically Determined Weights
    • 50
    • Open Access
    Support for linguistic macrofamilies from weighted sequence alignment
    • 24
    • Open Access
    Evolved structure of language shows lineage-specific trends in word-order universals
    • 359
    • Open Access
    Advances in automated language classification
    • 11
    • Open Access
    Language trees support the express-train sequence of Austronesian expansion
    • 333
    • Open Access
    Automatic cognate classification with a Support Vector Machine
    • 10
    • Highly Influential
    Automated reconstruction of ancient languages using probabilistic models of sound change
    • 96
    • Open Access