OP-IMS @ DIACR-Ita: Back to the Roots: SGNS+OP+CD still rocks Semantic Change Detection
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Abstract (arXiv)
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.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Nov 2020.
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Citing papers per year: 2021: 4; 2022: 1.
5 papers.
- 2021Explaining and Improving BERT Performance on Lexical Semantic Change Detectioncited by 33
- 2021Lexical Semantic Change Discoverycited by 13
- 2022LSCDiscovery: A shared task on semantic change discovery and detection in Spanishcited by 11
- 2021Effects of Pre- and Post-Processing on type-based Embeddings in Lexical Semantic Change Detectioncited by 4
- 2021On the Universality of Deep Contextual Language Modelscited by 4
- 2022LSCDiscovery: A shared task on semantic change discovery and detection in Spanish13 May 2022
- 2021On the Universality of Deep Contextual Language Models15 Sep 2021
- 2021Lexical Semantic Change Discovery6 Jun 2021
- 2021Explaining and Improving BERT Performance on Lexical Semantic Change Detection12 Mar 2021
- 2021Effects of Pre- and Post-Processing on type-based Embeddings in Lexical Semantic Change Detection22 Jan 2021