@inproceedings{aida-etal-2026-statistical,
title = "Statistical Semantic Change Detection via Usage Similarities",
author = "Aida, Taichi and
Mochihashi, Daichi and
Takamura, Hiroya and
Ogiso, Toshinobu and
Komachi, Mamoru",
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-1.2/",
pages = "20--26",
ISBN = "979-8-89176-362-3",
abstract = "Semantic change detection comprises two subtasks: classification, which predicts whether a target word has undergone a semantic shift, and ranking, which orders words according to the degree of their semantic change. While most prior studies concentrated on ranking subtask, the classification subtask plays an equally important role, since many practical scenarios require a yes/no decision on semantic change rather than a global ranking. In this work, we propose a novel statistical method that predicts the presence or absence of semantic change. While most existing approaches infer semantic change by comparing word embeddings across time periods or domains, our method directly models the diachronic/synchronic consistency of usage-level similarity scores. Our experiments on SemEval-2020 Task 1 and WUGS datasets demonstrate that the proposed formulation outperforms existing state-of-the-art embedding-based methods, and robustly detects semantic change across languages in both diachronic and synchronic settings."
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<abstract>Semantic change detection comprises two subtasks: classification, which predicts whether a target word has undergone a semantic shift, and ranking, which orders words according to the degree of their semantic change. While most prior studies concentrated on ranking subtask, the classification subtask plays an equally important role, since many practical scenarios require a yes/no decision on semantic change rather than a global ranking. In this work, we propose a novel statistical method that predicts the presence or absence of semantic change. While most existing approaches infer semantic change by comparing word embeddings across time periods or domains, our method directly models the diachronic/synchronic consistency of usage-level similarity scores. Our experiments on SemEval-2020 Task 1 and WUGS datasets demonstrate that the proposed formulation outperforms existing state-of-the-art embedding-based methods, and robustly detects semantic change across languages in both diachronic and synchronic settings.</abstract>
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%0 Conference Proceedings
%T Statistical Semantic Change Detection via Usage Similarities
%A Aida, Taichi
%A Mochihashi, Daichi
%A Takamura, Hiroya
%A Ogiso, Toshinobu
%A Komachi, Mamoru
%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 aida-etal-2026-statistical
%X Semantic change detection comprises two subtasks: classification, which predicts whether a target word has undergone a semantic shift, and ranking, which orders words according to the degree of their semantic change. While most prior studies concentrated on ranking subtask, the classification subtask plays an equally important role, since many practical scenarios require a yes/no decision on semantic change rather than a global ranking. In this work, we propose a novel statistical method that predicts the presence or absence of semantic change. While most existing approaches infer semantic change by comparing word embeddings across time periods or domains, our method directly models the diachronic/synchronic consistency of usage-level similarity scores. Our experiments on SemEval-2020 Task 1 and WUGS datasets demonstrate that the proposed formulation outperforms existing state-of-the-art embedding-based methods, and robustly detects semantic change across languages in both diachronic and synchronic settings.
%U https://aclanthology.org/2026.lchange-1.2/
%P 20-26
Markdown (Informal)
[Statistical Semantic Change Detection via Usage Similarities](https://aclanthology.org/2026.lchange-1.2/) (Aida et al., LChange 2026)
ACL
- Taichi Aida, Daichi Mochihashi, Hiroya Takamura, Toshinobu Ogiso, and Mamoru Komachi. 2026. Statistical Semantic Change Detection via Usage Similarities. In The Proceedings for the 6th International Workshop on Computational Approaches to Language Change (LChange’26), pages 20–26, Rabat, Morocco. Association for Computational Linguistics.