@inproceedings{herman-rychly-2026-detecting,
title = "Detecting Subtle Sense Shift with Polysemy-Aware Trends",
author = "Herman, Ond{\v{r}}ej and
Rychl{\'y}, Pavel",
editor = "Demberg, Vera and
Inui, Kentaro and
Marquez, Llu{\'i}s",
booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 2: Short Papers)",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.eacl-short.2/",
pages = "60--65",
ISBN = "979-8-89176-381-4",
abstract = "Language changes faster than dictionaries can be revised, yet automatic tools still struggle to spot the subtle, short-term shifts in meaning that precede a formal update. We present a language-independent pipeline that detects word-sense shifts in large, time-stamped web corpora. The method couples a robust re-implementation of the Adaptive Skip-Gram model, which induces multiple sense vectors per lemma without any external inventory, with a second stage that tracks each sense through time under three alternative frequency normalizations. Linear Regression and the robust Mann-Kendall/Theil-Sen estimator then test whether a sense{'}s frequency slope deviates significantly from zero, producing a ranked list of headwords whose semantics are drifting.We evaluate the system on the English (12 B tokens) and Czech (1 B tokens) Timestamped corpora for May 2023-May 2025. Expert annotation of the top-100 candidates for each model variant shows that 50.7{\%} of Czech and 25.7{\%} of English headwords exhibit genuine sense shifts, despite web-scale noise."
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%0 Conference Proceedings
%T Detecting Subtle Sense Shift with Polysemy-Aware Trends
%A Herman, Ondřej
%A Rychlý, Pavel
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-381-4
%F herman-rychly-2026-detecting
%X Language changes faster than dictionaries can be revised, yet automatic tools still struggle to spot the subtle, short-term shifts in meaning that precede a formal update. We present a language-independent pipeline that detects word-sense shifts in large, time-stamped web corpora. The method couples a robust re-implementation of the Adaptive Skip-Gram model, which induces multiple sense vectors per lemma without any external inventory, with a second stage that tracks each sense through time under three alternative frequency normalizations. Linear Regression and the robust Mann-Kendall/Theil-Sen estimator then test whether a sense’s frequency slope deviates significantly from zero, producing a ranked list of headwords whose semantics are drifting.We evaluate the system on the English (12 B tokens) and Czech (1 B tokens) Timestamped corpora for May 2023-May 2025. Expert annotation of the top-100 candidates for each model variant shows that 50.7% of Czech and 25.7% of English headwords exhibit genuine sense shifts, despite web-scale noise.
%U https://aclanthology.org/2026.eacl-short.2/
%P 60-65
Markdown (Informal)
[Detecting Subtle Sense Shift with Polysemy-Aware Trends](https://aclanthology.org/2026.eacl-short.2/) (Herman & Rychlý, EACL 2026)
ACL
- Ondřej Herman and Pavel Rychlý. 2026. Detecting Subtle Sense Shift with Polysemy-Aware Trends. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers), pages 60–65, Rabat, Morocco. Association for Computational Linguistics.