Towards identifying the optimal datasize for lexically-based Bayesian inference of linguistic phylogenies

Taraka Rama, Søren Wichmann


Abstract
Bayesian linguistic phylogenies are standardly based on cognate matrices for words referring to a fix set of meanings—typically around 100-200. To this day there has not been any empirical investigation into which datasize is optimal. Here we determine, across a set of language families, the optimal number of meanings required for the best performance in Bayesian phylogenetic inference. We rank meanings by stability, infer phylogenetic trees using first the most stable meaning, then the two most stable meanings, and so on, computing the quartet distance of the resulting tree to the tree proposed by language family experts at each step of datasize increase. When a gold standard tree is not available we propose to instead compute the quartet distance between the tree based on the n-most stable meaning and the one based on the n + 1-most stable meanings, increasing n from 1 to N − 1, where N is the total number of meanings. The assumption here is that the value of n for which the quartet distance begins to stabilize is also the value at which the quality of the tree ceases to improve. We show that this assumption is borne out. The results of the two methods vary across families, and the optimal number of meanings appears to correlate with the number of languages under consideration.
Anthology ID:
C18-1134
Volume:
Proceedings of the 27th International Conference on Computational Linguistics
Month:
August
Year:
2018
Address:
Santa Fe, New Mexico, USA
Editors:
Emily M. Bender, Leon Derczynski, Pierre Isabelle
Venue:
COLING
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1578–1590
Language:
URL:
https://aclanthology.org/C18-1134
DOI:
Bibkey:
Cite (ACL):
Taraka Rama and Søren Wichmann. 2018. Towards identifying the optimal datasize for lexically-based Bayesian inference of linguistic phylogenies. In Proceedings of the 27th International Conference on Computational Linguistics, pages 1578–1590, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
Cite (Informal):
Towards identifying the optimal datasize for lexically-based Bayesian inference of linguistic phylogenies (Rama & Wichmann, COLING 2018)
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PDF:
https://aclanthology.org/C18-1134.pdf