OP-IMS @ DIACR-Ita: Back to the Roots: SGNS+OP+CD still rocks Semantic Change Detection

TL;DR

This paper demonstrates that traditional type-based semantic change detection methods, combining SGNS, Orthogonal Procrustes, and Cosine Distance, achieve near-perfect accuracy and outperform newer approaches in Italian lexical semantic change detection.

Contribution

The paper presents a winning approach for lexical semantic change detection using classic models, highlighting their continued effectiveness in this task.

Findings

Achieved near 94% accuracy in DIACR-Ita shared task

Traditional type-based methods outperform recent approaches

Validated the effectiveness of SGNS+OP+CD in semantic change detection

Abstract

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