DiaSense at SemEval-2020 Task 1: Modeling sense change via pre-trained BERT embeddings

Abstract

This paper describes DiaSense, a system developed for Task 1 'Unsupervised Lexical Semantic Change Detection' of SemEval-2020. In DiaSense, contextualized word embeddings are used to model word sense changes. This allows for the calculation of metrics which mimic human intuitions about the semantic relatedness between individual use pairs of a target word for the assessment of lexical semantic change. DiaSense is able to detect lexical semantic change in English, German, Latin and Swedish (accuracy = 0.728). Moreover, DiaSense differentiates between weak and strong change.