Diachronic Usage Relatedness (DURel): A Framework for the Annotation of Lexical Semantic Change
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Dominik Schlechtweg, Sabine Schulte im Walde, Stefanie Eckmann. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers). 2018.
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The task's corpus includes pairs of sentences rated from 1 to 4, where 1 indicates identical word usage and 4 indicates completely different usage.
“The task's corpus includes pairs of sentences rated from 1 to 4, where 1 indicates identical word usage and 4 indicates completely different usage (Schlechtweg et al., 2018).”
From Historical Ink: Semantic Shift Detection for 19th Century Spanish (2024) · cites this paper as Schlechtweg et al., 2018
The Sampling section says the study used pairs of sentences containing a target word. But Table 2 gives the opposite rating direction from the claim: 4 means “Identical” and 1 means “Unrelated.” (LLM, conf=0.94)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredThe LSCDiscovery shared task provides over 65,000 annotated examples for 100 target words using the DURel framework.
“The LSCDiscovery shared task (Zamora-Reina et al., 2022) provides over 65,000 annotated examples for 100 target words using the DURel framework proposed by Schlechtweg et al. (2018).”
From Historical Ink: Semantic Shift Detection for 19th Century Spanish (2024) · cites this paper as Schlechtweg et al., 2018
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Self-citationAutomatic systems with word sense induction for lexical semantic change detection
Kokosinskii, D. V., & Arefyev, N. V. (2026) · Vyčislitelʹnye metody i programmirovanie · doi:10.26089/nummet.v27r329
Large Language Models for Word-In-Context
Kokosinskii, D. V. (2026) · Automatic Documentation and Mathematical Linguistics · doi:10.3103/s0005105526700743
Large Language Models for Word-In-Context
Kokosinskii, D. V. (2026) · Russian Digital Libraries Journal · doi:10.26907/1562-5419-2026-29-4-1133-1154
- Authors
- Schlechtweg, D., Schulte im Walde, S., & Eckmann, S.
- Affiliations
- University of Stuttgart (DE)
- ORCID
- Schlechtweg, Dominik
- Venue
- Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
- Published
- 2018
- Pages
- 169-174
- OpenAlex
- W2796841076
- Cited by
- 77
- References
- 12
- Citations by year
- 2026: 4 · 2025: 3 · 2024: 5 · 2023: 6 · 2022: 8 · 2021: 24 · 2020: 24 · 2019: 3
- FWCI
- 5.29
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Deutsche Forschungsgemeinschaft (SFB732)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Social Sciences › Arts and Humanities › Language and Linguistics › Lexicography and Language Studies
- Social Sciences › Arts and Humanities › Language and Linguistics › Linguistics, Language Diversity, and Identity
- Keywords
- lexical semantic change, annotation framework, semantic change, historical linguistics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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