Uncovering Probabilistic Implications in Typological Knowledge Bases
This addresses the time-consuming manual work in linguistic typology, potentially revealing unexplored universals, though it appears incremental as it builds on prior computational methods.
The paper tackles the problem of manually uncovering linguistic implications in typology by presenting a computational model that identifies known universals and discovers new ones, outperforming existing baselines.
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 post-positions. 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.