# CL-IMS @ DIACR-Ita: Volente o Nolente: BERT does not outperform SGNS on Semantic Change Detection

arXiv: https://arxiv.org/abs/2011.07247 · HTML: https://mlsanity.com/paper/2011.07247/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Nov 2020.

## Built on by (most cited first, 4 of 4)

- [A Survey on Contextualised Semantic Shift Detection](https://mlsanity.com/paper/2304.01666/) (2023)
- [Lexical Semantic Change Discovery](https://mlsanity.com/paper/2106.03111/) (2021)
- [Effects of Pre- and Post-Processing on type-based Embeddings in Lexical Semantic Change Detection](https://mlsanity.com/paper/2101.09368/) (2021)
- [Words and Action: Modeling Linguistic Leadership in #BlackLivesMatter Communities](https://mlsanity.com/paper/2412.02637/) (2024)

## Abstract (arXiv)

We present the results of our participation in the DIACR-Ita shared task on lexical semantic change detection for Italian. We exploit Average Pairwise Distance of token-based BERT embeddings between time points and rank 5 (of 8) in the official ranking with an accuracy of .72. While we tune parameters on the English data set of SemEval-2020 Task 1 and reach high performance, this does not translate to the Italian DIACR-Ita data set. Our results show that we do not manage to find robust ways to exploit BERT embeddings in lexical semantic change detection.