  We present our systems and findings on unsupervised lexical semantic change
for the Italian language in the DIACR-Ita shared-task at EVALITA 2020. The task
is to determine whether a target word has evolved its meaning with time, only
relying on raw-text from two time-specific datasets. We propose two models
representing the target words across the periods to predict the changing words
using threshold and voting schemes. Our first model solely relies on
part-of-speech usage and an ensemble of distance measures. The second model
uses word embedding representation to extract the neighbor's relative distances
across spaces and propose "the average of absolute differences" to estimate
lexical semantic change. Our models achieved competent results, ranking third
in the DIACR-Ita competition. Furthermore, we experiment with the k_neighbor
parameter of our second model to compare the impact of using "the average of
absolute differences" versus the cosine distance used in Hamilton et al.
(2016).
