  This paper describes the system proposed for the SemEval-2020 Task 1:
Unsupervised Lexical Semantic Change Detection. We focused our approach on the
detection problem. Given the semantics of words captured by temporal word
embeddings in different time periods, we investigate the use of unsupervised
methods to detect when the target word has gained or loosed senses. To this
end, we defined a new algorithm based on Gaussian Mixture Models to cluster the
target similarities computed over the two periods. We compared the proposed
approach with a number of similarity-based thresholds. We found that, although
the performance of the detection methods varies across the word embedding
algorithms, the combination of Gaussian Mixture with Temporal Referencing
resulted in our best system.
