Work

Analyzing Correlated Evolution of Multiple Features Using Latent Representations

Year: 2018

Type: conference-paper

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.

Source: Conference on Empirical Methods in Natural Language Processing (EMNLP)

DOI: 10.18653/v1/d18-1468

Author: Yugo Murawaki

Institution: Kyoto University

Language: English

Topic: Language and cultural evolution

Subfield: Cultural Studies

Field: Social Sciences

Domain: Social Sciences

Open Access status: hybrid

Funder: Japan Society for the Promotion of Science

Award: Computational approaches to understanding historical changes of languages