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Simulating Sense Extension

Paper · 2026 · Rowan Hall Maudslay and Simone Teufel

Summary

People routinely reuse familiar words for unfamiliar things through metaphor, metonymy, slang, and similar processes. Computational work on such sense extension has lacked a shared, controlled way to train and test generative models. Existing tasks are typically single-phenomenon, mostly text-only, and sometimes leak evaluation material into training. The authors propose novel sense formulation (NSF). A model learns from a deliberately incomplete world, a "pruniverse," in which some concepts have been removed. At test time it must pick an existing word to name a held-out concept. Concepts are represented with multimodal, lexicon-agnostic embeddings, which keeps word knowledge from leaking into the representations. Seven pruniverses cover different extension types. Three models were compared: a perceptron, a transformer, and a well-known cognitive model. A multimodal perceptron came out on top for nearly every phenomenon. The cognitive model fell clearly behind, which suggests its account of sense extension is incomplete. NSF gives a single benchmark for comparing models across phenomena and modalities, including tests of whether LLMs generalise in a human-like way.

Key points

  • The seven pruniverses cover metaphor, metonymy, semantic drift, jargon, slang, conversion, and overextension in child language.
  • The models draw on four concept embedding spaces (representational modalities). Visual data includes ImageNet images, and the resources are linked to WordNet, including a BabelNet–WordNet mapping.
  • The multimodal perceptron was best on every phenomenon except one. It did best on metonymy and had the most difficulty with semantic drift.
  • A follow-up analysis of prototype effects backed the conclusion that the cognitive model falls short as an account of how humans extend word senses.
  • Code and data are released at github.com/rowanhm/pruniverse. NSF covers only the formulation stage of word production, not conceptualisation.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓University of CambridgeCambridge · United Kingdom243 works · 121,396 citations🎓Université Paris Sciences et LettresParis · France21 works · 3,290 citations🎓École Nationale des ChartesParis · France3 works · 14 citations

 Fields, topics and concepts

📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksComputer Vision and Pattern Recognition · 2,869 worksPsychology · 1,552 worksExperimental and Cognitive Psychology · 870 works

Cite

Cite as Rowan Hall Maudslay and Simone Teufel. 2026. Simulating Sense Extension. Transactions of the Association for Computational Linguistics.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1162/tacl.a.780

@article{maudslay2026simulating,
  title = {{Simulating Sense Extension}},
  author = {Rowan Hall Maudslay and Simone Teufel},
  journal = {Transactions of the Association for Computational Linguistics},
  year = {2026},
  doi = {10.1162/tacl.a.780},
  url = {https://doi.org/10.1162/tacl.a.780}
}

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