Transparent Semantic Change Detection with Dependency-Based Profiles

Bach Phan Tat; Kris Heylen; Dirk Geeraerts; Stefano De Pascale; Dirk Speelman

Transparent Semantic Change Detection with Dependency-Based Profiles

Bach Phan Tat, Kris Heylen, Dirk Geeraerts, Stefano De Pascale, Dirk Speelman

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Abstract

Most modern computational approaches to lexical semantic change detection (LSC) rely on embedding-based distributional word representations with neural networks. Despite the strong performance on LSC benchmarks, they are often opaque. We investigate an alternative method which relies purely on dependency co-occurrence patterns of words. We demonstrate that it is effective for semantic change detection and even outperforms a number of distributional semantic models. We provide an in-depth quantitative and qualitative analysis of the predictions, showing that they are plausible and interpretable.

Anthology ID:: 2026.lchange-1.8
Volume:: The Proceedings for the 6th International Workshop on Computational Approaches to Language Change (LChange’26)
Month:: March
Year:: 2026
Address:: Rabat, Morocco
Editors:: Nina Tahmasebi, Pierluigi Cassotti, Syrielle Montariol, Andrey Kutuzov, Netta Huebscher, Elena Spaziani, Naomi Baes
Venues:: LChange | WS
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 97–109
Language:
URL:: https://aclanthology.org/2026.lchange-1.8/
DOI:
Bibkey:
Cite (ACL):: Bach Phan Tat, Kris Heylen, Dirk Geeraerts, Stefano De Pascale, and Dirk Speelman. 2026. Transparent Semantic Change Detection with Dependency-Based Profiles. In The Proceedings for the 6th International Workshop on Computational Approaches to Language Change (LChange’26), pages 97–109, Rabat, Morocco. Association for Computational Linguistics.
Cite (Informal):: Transparent Semantic Change Detection with Dependency-Based Profiles (Tat et al., LChange 2026)
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PDF:: https://aclanthology.org/2026.lchange-1.8.pdf

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@inproceedings{tat-etal-2026-transparent,
    title = "Transparent Semantic Change Detection with Dependency-Based Profiles",
    author = "Tat, Bach Phan  and
      Heylen, Kris  and
      Geeraerts, Dirk  and
      De Pascale, Stefano  and
      Speelman, Dirk",
    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-1.8/",
    pages = "97--109",
    ISBN = "979-8-89176-362-3",
    abstract = "Most modern computational approaches to lexical semantic change detection (LSC) rely on embedding-based distributional word representations with neural networks. Despite the strong performance on LSC benchmarks, they are often opaque. We investigate an alternative method which relies purely on dependency co-occurrence patterns of words. We demonstrate that it is effective for semantic change detection and even outperforms a number of distributional semantic models. We provide an in-depth quantitative and qualitative analysis of the predictions, showing that they are plausible and interpretable."
}

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%0 Conference Proceedings
%T Transparent Semantic Change Detection with Dependency-Based Profiles
%A Tat, Bach Phan
%A Heylen, Kris
%A Geeraerts, Dirk
%A De Pascale, Stefano
%A Speelman, Dirk
%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 tat-etal-2026-transparent
%X Most modern computational approaches to lexical semantic change detection (LSC) rely on embedding-based distributional word representations with neural networks. Despite the strong performance on LSC benchmarks, they are often opaque. We investigate an alternative method which relies purely on dependency co-occurrence patterns of words. We demonstrate that it is effective for semantic change detection and even outperforms a number of distributional semantic models. We provide an in-depth quantitative and qualitative analysis of the predictions, showing that they are plausible and interpretable.
%U https://aclanthology.org/2026.lchange-1.8/
%P 97-109

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Markdown (Informal)

[Transparent Semantic Change Detection with Dependency-Based Profiles](https://aclanthology.org/2026.lchange-1.8/) (Tat et al., LChange 2026)

Transparent Semantic Change Detection with Dependency-Based Profiles (Tat et al., LChange 2026)

ACL

Bach Phan Tat, Kris Heylen, Dirk Geeraerts, Stefano De Pascale, and Dirk Speelman. 2026. Transparent Semantic Change Detection with Dependency-Based Profiles. In The Proceedings for the 6th International Workshop on Computational Approaches to Language Change (LChange’26), pages 97–109, Rabat, Morocco. Association for Computational Linguistics.