Automatic Detection of Word Sense Shift from Corpus Data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F25%3A00142718" target="_blank" >RIV/00216224:14330/25:00142718 - isvavai.cz</a>

Výsledek na webu

<a href="https://elex.link/elex2025/proceedings/" target="_blank" >https://elex.link/elex2025/proceedings/</a>

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

Automatic Detection of Word Sense Shift from Corpus Data

Popis výsledku v původním jazyce

Language evolves continuously, rendering static dictionaries quickly outdated. While previous research has addressed the automatic detection of new words, identifying subtler semantic changes in existing words remains a challenge. In this work, we propose a robust, language-independent methodology for the automatic detection of word sense shifts using diachronic corpus data. Our approach builds on the Adaptive Skip-Gram algorithm for word sense induction, enabling us to model polysemy directly from raw text without reliance on external sense inventories. We calculate the temporal distribution of induced senses and apply trend estimation techniques—specifically linear regression and the Theil–Sen estimator—to detect statistically significant shifts. This two-stage architecture decouples sense induction from trend analysis, increasing overall robustness and interpretability. Unlike traditional methods in lexical semantic change detection, which often target dramatic historical shifts, our method is designed to detect emerging or evolving senses over shorter timescales using large web corpora. We evaluate our method on Timestamped corpora in English and Czech and present several examples of detected sense shifts. The results demonstrate the feasibility of scalable, automatic sense shift detection and its potential applications in lexicography and linguistic research.

Název v anglickém jazyce

Automatic Detection of Word Sense Shift from Corpus Data

Popis výsledku anglicky

Language evolves continuously, rendering static dictionaries quickly outdated. While previous research has addressed the automatic detection of new words, identifying subtler semantic changes in existing words remains a challenge. In this work, we propose a robust, language-independent methodology for the automatic detection of word sense shifts using diachronic corpus data. Our approach builds on the Adaptive Skip-Gram algorithm for word sense induction, enabling us to model polysemy directly from raw text without reliance on external sense inventories. We calculate the temporal distribution of induced senses and apply trend estimation techniques—specifically linear regression and the Theil–Sen estimator—to detect statistically significant shifts. This two-stage architecture decouples sense induction from trend analysis, increasing overall robustness and interpretability. Unlike traditional methods in lexical semantic change detection, which often target dramatic historical shifts, our method is designed to detect emerging or evolving senses over shorter timescales using large web corpora. We evaluate our method on Timestamped corpora in English and Czech and present several examples of detected sense shifts. The results demonstrate the feasibility of scalable, automatic sense shift detection and its potential applications in lexicography and linguistic research.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

9th Biennial Conference on Electronic Lexicography in the 21st Century, eLex 2025

ISBN

—

ISSN

2533-5626

e-ISSN

—

Počet stran výsledku

15

Strana od-do

237-251

Název nakladatele

Lexical Computing CZ s.r.o.

Místo vydání

Bled, Slovenia

Místo konání akce

Bled, Slovenia

Datum konání akce

18. 11. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—