We present the results of our participation in the DIACR-Ita shared task on\nlexical semantic change detection for Italian. We exploit one of the earliest\nand most influential semantic change detection models based on Skip-Gram with\nNegative Sampling, Orthogonal Procrustes alignment and Cosine Distance and\nobtain the winning submission of the shared task with near to perfect accuracy\n.94. Our results once more indicate that, within the present task setup in\nlexical semantic change detection, the traditional type-based approaches yield\nexcellent performance.\n