  In this paper, we focus on the detection of semantic changes in Slovene, a
less resourced Slavic language with two million speakers. Detecting and
tracking semantic changes provides insights into the evolution of the language
caused by changes in society and culture. Recently, several systems have been
proposed to aid in this study, but all depend on manually annotated gold
standard datasets for evaluation. In this paper, we present the first Slovene
dataset for evaluating semantic change detection systems, which contains
aggregated semantic change scores for 104 target words obtained from more than
3000 manually annotated sentence pairs. We evaluate several existing semantic
change detection methods on this dataset and also propose a novel approach
based on optimal transport that improves on the existing state-of-the-art
systems with an error reduction rate of 22.8%.
