Uncovering Probabilistic Implications in Typological Knowledge Bases

Johannes Bjerva, Yova Kementchedjhieva, Ryan Cotterell, Isabelle Augenstein


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.
Anthology ID:
P19-1382
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3924–3930
URL:
https://www.aclweb.org/anthology/P19-1382
DOI:
10.18653/v1/P19-1382
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PDF:
https://www.aclweb.org/anthology/P19-1382.pdf