  We present the results of our system for SemEval-2020 Task 1 that exploits a
commonly used lexical semantic change detection model based on Skip-Gram with
Negative Sampling. Our system focuses on Vector Initialization (VI) alignment,
compares VI to the currently top-ranking models for Subtask 2 and demonstrates
that these can be outperformed if we optimize VI dimensionality. We demonstrate
that differences in performance can largely be attributed to model-specific
sources of noise, and we reveal a strong relationship between dimensionality
and frequency-induced noise in VI alignment. Our results suggest that lexical
semantic change models integrating vector space alignment should pay more
attention to the role of the dimensionality parameter.
