Exploring cross-linguistic contextual predictors of colexification

Abstract

Colexification, a linguistic phenomenon where one word expresses multiple meanings, permeates natural language. Despite its ubiquity, its underlying mechanisms are still largely unknown. This study explores the effect of different proxies of context confusability, specifically cosine similarity and symmetrized Kullback-Leibler divergence, through a cross-linguistic analysis using contextualized embeddings for English and Spanish data. By analyzing the distribution of raw data and fitting various logistic regression models, we examined the behavior of these measures in the context of colexification and tested their predictive capacity. Our findings are consistent across both English and Spanish, with slight variations between them. Both predictors were found to hold information for the prediction of colexification, effectively working as proxies of contextual confusability through all sets of contextualized embeddings. This study contributes to a deeper understanding of the circumstances under which colexification occurs, offering new insights into the factors influencing this linguistic phenomenon.