University of Padova @ DIACR-Ita

Abstract

Semantic change detection task in a relatively low-resource language like Italian is challenging. By using contextualized word embeddings, we formalize the task as a distance metric for two flexible-size sets of vectors. Various distance metrics like average Euclidean Distance, average Canberra distance, Hausdorff distance, as well as Jensen–Shannon divergence between cluster distributions based on K-means clustering and Gaussian mixture model are used. The final prediction is given by an ensemble of top-ranked words based on each distance metric. The proposed method achieved better performance than a frequency and collocation based baselines.