Uncovering Probabilistic Implications in Typological Knowledge Bases
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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Uncovering Probabilistic Implications in Typological Knowledge Bases. / Bjerva, Johannes; Kementchedjhieva, Yova Radoslavova; Cotterell, Ryan ; Augenstein, Isabelle.
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 2019. s. 3924–3930.Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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TY - GEN
T1 - Uncovering Probabilistic Implications in Typological Knowledge Bases
AU - Bjerva, Johannes
AU - Kementchedjhieva, Yova Radoslavova
AU - Cotterell, Ryan
AU - Augenstein, Isabelle
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
U2 - 10.18653/v1/P19-1382
DO - 10.18653/v1/P19-1382
M3 - Article in proceedings
SP - 3924
EP - 3930
BT - Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
PB - Association for Computational Linguistics
T2 - 57th Annual Meeting of the Association for Computational Linguistics
Y2 - 1 July 2019 through 1 July 2019
ER -
ID: 239205117