@inproceedings{goworek-dubossarsky-2026-rethinking,
title = "Rethinking Metrics for Lexical Semantic Change Detection",
author = "Goworek, Roksana and
Dubossarsky, Haim",
editor = "Tahmasebi, Nina and
Cassotti, Pierluigi and
Montariol, Syrielle and
Kutuzov, Andrey and
Huebscher, Netta and
Spaziani, Elena and
Baes, Naomi",
booktitle = "The Proceedings for the 6th International Workshop on Computational Approaches to Language Change ({LC}hange{'}26)",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.lchange-1.13/",
pages = "147--161",
ISBN = "979-8-89176-362-3",
abstract = "Lexical semantic change detection (LSCD) increasingly relies on contextualised language model embeddings, yet most approaches still quantify change using a small set of semantic change metrics, primarily Average Pairwise Distance (APD) and cosine distance over word prototypes (PRT). We introduce Average Minimum Distance (AMD) and Symmetric Average Minimum Distance (SAMD), new measures that quantify semantic change via local correspondence between word usages across time periods. Across multiple languages, encoder models, and representation spaces, we show that AMD often provides more robust performance, particularly under dimensionality reduction and with non-specialised encoders, while SAMD excels with specialised encoders. We suggest that LSCD may benefit from considering alternative semantic change metrics beyond APD and PRT, with AMD offering a robust option for contextualised embedding-based analysis."
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<abstract>Lexical semantic change detection (LSCD) increasingly relies on contextualised language model embeddings, yet most approaches still quantify change using a small set of semantic change metrics, primarily Average Pairwise Distance (APD) and cosine distance over word prototypes (PRT). We introduce Average Minimum Distance (AMD) and Symmetric Average Minimum Distance (SAMD), new measures that quantify semantic change via local correspondence between word usages across time periods. Across multiple languages, encoder models, and representation spaces, we show that AMD often provides more robust performance, particularly under dimensionality reduction and with non-specialised encoders, while SAMD excels with specialised encoders. We suggest that LSCD may benefit from considering alternative semantic change metrics beyond APD and PRT, with AMD offering a robust option for contextualised embedding-based analysis.</abstract>
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%0 Conference Proceedings
%T Rethinking Metrics for Lexical Semantic Change Detection
%A Goworek, Roksana
%A Dubossarsky, Haim
%Y Tahmasebi, Nina
%Y Cassotti, Pierluigi
%Y Montariol, Syrielle
%Y Kutuzov, Andrey
%Y Huebscher, Netta
%Y Spaziani, Elena
%Y Baes, Naomi
%S The Proceedings for the 6th International Workshop on Computational Approaches to Language Change (LChange’26)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-362-3
%F goworek-dubossarsky-2026-rethinking
%X Lexical semantic change detection (LSCD) increasingly relies on contextualised language model embeddings, yet most approaches still quantify change using a small set of semantic change metrics, primarily Average Pairwise Distance (APD) and cosine distance over word prototypes (PRT). We introduce Average Minimum Distance (AMD) and Symmetric Average Minimum Distance (SAMD), new measures that quantify semantic change via local correspondence between word usages across time periods. Across multiple languages, encoder models, and representation spaces, we show that AMD often provides more robust performance, particularly under dimensionality reduction and with non-specialised encoders, while SAMD excels with specialised encoders. We suggest that LSCD may benefit from considering alternative semantic change metrics beyond APD and PRT, with AMD offering a robust option for contextualised embedding-based analysis.
%U https://aclanthology.org/2026.lchange-1.13/
%P 147-161
Markdown (Informal)
[Rethinking Metrics for Lexical Semantic Change Detection](https://aclanthology.org/2026.lchange-1.13/) (Goworek & Dubossarsky, LChange 2026)
ACL
- Roksana Goworek and Haim Dubossarsky. 2026. Rethinking Metrics for Lexical Semantic Change Detection. In The Proceedings for the 6th International Workshop on Computational Approaches to Language Change (LChange’26), pages 147–161, Rabat, Morocco. Association for Computational Linguistics.