Abstract
Statistical phylogenetic models have allowed the quantitative analysis of the evolution of a single categorical feature and a pair of binary features, but correlated evolution involving multiple discrete features is yet to be explored. Here we propose latent representation-based analysis in which (1) a sequence of discrete surface features is projected to a sequence of independent binary variables and (2) phylogenetic inference is performed on the latent space. In the experiments, we analyze the features of linguistic typology, with a special focus on the order of subject, object and verb. Our analysis suggests that languages sharing the same word order are not necessarily a coherent group but exhibit varying degrees of diachronic stability depending on other features.- Anthology ID:
- D18-1468
- Volume:
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Month:
- October-November
- Year:
- 2018
- Address:
- Brussels, Belgium
- Editors:
- Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4371–4382
- Language:
- URL:
- https://aclanthology.org/D18-1468
- DOI:
- 10.18653/v1/D18-1468
- Cite (ACL):
- Yugo Murawaki. 2018. Analyzing Correlated Evolution of Multiple Features Using Latent Representations. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4371–4382, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Analyzing Correlated Evolution of Multiple Features Using Latent Representations (Murawaki, EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/D18-1468.pdf
- Code
- murawaki/lattyp