  In this paper, we present the results and main findings of our system for the
DIACR-ITA 2020 Task. Our system focuses on using variations of training sets
and different semantic detection methods. The task involves training, aligning
and predicting a word's vector change from two diachronic Italian corpora. We
demonstrate that using Temporal Word Embeddings with a Compass C-BOW model is
more effective compared to different approaches including Logistic Regression
and a Feed Forward Neural Network using accuracy. Our model ranked 3rd with an
accuracy of 83.3%.
