@inproceedings{bjerva-etal-2019-uncovering,
    title = "Uncovering Probabilistic Implications in Typological Knowledge Bases",
    author = "Bjerva, Johannes  and
      Kementchedjhieva, Yova  and
      Cotterell, Ryan  and
      Augenstein, Isabelle",
    editor = "Korhonen, Anna  and
      Traum, David  and
      M{\`a}rquez, Llu{\'i}s",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Author-page-Marten-During-lu/P19-1382/",
    doi = "10.18653/v1/P19-1382",
    pages = "3924--3930",
    abstract = "The study of linguistic typology is rooted in the implications we find between linguistic features, such as the fact that languages with object-verb word ordering tend to have postpositions. Uncovering such implications typically amounts to time-consuming manual processing by trained and experienced linguists, which potentially leaves key linguistic universals unexplored. In this paper, we present a computational model which successfully identifies known universals, including Greenberg universals, but also uncovers new ones, worthy of further linguistic investigation. Our approach outperforms baselines previously used for this problem, as well as a strong baseline from knowledge base population."
}