Measuring the Similarity of Grammatical Gender Systems by Comparing Partitions

Arya D. McCarthy, Adina Williams, Shijia Liu, David Yarowsky, Ryan Cotterell


Abstract
A grammatical gender system divides a lexicon into a small number of relatively fixed grammatical categories. How similar are these gender systems across languages? To quantify the similarity, we define gender systems extensionally, thereby reducing the problem of comparisons between languages’ gender systems to cluster evaluation. We borrow a rich inventory of statistical tools for cluster evaluation from the field of community detection (Driver and Kroeber, 1932; Cattell, 1945), that enable us to craft novel information theoretic metrics for measuring similarity between gender systems. We first validate our metrics, then use them to measure gender system similarity in 20 languages. We then ask whether our gender system similarities alone are sufficient to reconstruct historical relationships between languages. Towards this end, we make phylogenetic predictions on the popular, but thorny, problem from historical linguistics of inducing a phylogenetic tree over extant Indo-European languages. Of particular interest, languages on the same branch of our phylogenetic tree are notably similar, whereas languages from separate branches are no more similar than chance.
Anthology ID:
2020.emnlp-main.456
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Editors:
Bonnie Webber, Trevor Cohn, Yulan He, Yang Liu
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5664–5675
Language:
URL:
https://aclanthology.org/2020.emnlp-main.456
DOI:
10.18653/v1/2020.emnlp-main.456
Bibkey:
Cite (ACL):
Arya D. McCarthy, Adina Williams, Shijia Liu, David Yarowsky, and Ryan Cotterell. 2020. Measuring the Similarity of Grammatical Gender Systems by Comparing Partitions. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 5664–5675, Online. Association for Computational Linguistics.
Cite (Informal):
Measuring the Similarity of Grammatical Gender Systems by Comparing Partitions (McCarthy et al., EMNLP 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.emnlp-main.456.pdf
Video:
 https://slideslive.com/38939365