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        <title>Modeling the Evolution of English Noun Compounds with Feature-Rich Diachronic Compositionality Prediction</title>
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        <namePart type="given">Filip</namePart>
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            <namePart type="given">Wanxiang</namePart>
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            <namePart type="given">Ekaterina</namePart>
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    <abstract>We analyze the evolution of English noun compounds, which we represent as vectors of time-specific values. We implement a wide array of methods to create a rich set of features, using them to classify compounds for present-day compositionality and to assess the informativeness of the corresponding linguistic patterns. Our best results use BERT – reflecting the similarity of compounds and sentence contexts – and we further capture relevant and complementary information across approaches. Leveraging these feature differences, we find that the development of low-compositional meanings is reflected by a parallel drop in compositionality and sustained semantic change. The same distinction is echoed in transformer processing: compositionality estimates require far less contextualization than semantic change estimates.</abstract>
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    <identifier type="doi">10.18653/v1/2025.acl-long.984</identifier>
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%0 Conference Proceedings
%T Modeling the Evolution of English Noun Compounds with Feature-Rich Diachronic Compositionality Prediction
%A Miletić, Filip
%A Schulte im Walde, Sabine
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F miletic-schulte-im-walde-2025-modeling
%X We analyze the evolution of English noun compounds, which we represent as vectors of time-specific values. We implement a wide array of methods to create a rich set of features, using them to classify compounds for present-day compositionality and to assess the informativeness of the corresponding linguistic patterns. Our best results use BERT – reflecting the similarity of compounds and sentence contexts – and we further capture relevant and complementary information across approaches. Leveraging these feature differences, we find that the development of low-compositional meanings is reflected by a parallel drop in compositionality and sustained semantic change. The same distinction is echoed in transformer processing: compositionality estimates require far less contextualization than semantic change estimates.
%R 10.18653/v1/2025.acl-long.984
%U https://aclanthology.org/2025.acl-long.984/
%U https://doi.org/10.18653/v1/2025.acl-long.984
%P 20071-20092

Markdown (Informal)

[Modeling the Evolution of English Noun Compounds with Feature-Rich Diachronic Compositionality Prediction](https://aclanthology.org/2025.acl-long.984/) (Miletić & Schulte im Walde, ACL 2025)

• Modeling the Evolution of English Noun Compounds with Feature-Rich Diachronic Compositionality Prediction (Miletić & Schulte im Walde, ACL 2025)

ACL

• Filip Miletić and Sabine Schulte im Walde. 2025. Modeling the Evolution of English Noun Compounds with Feature-Rich Diachronic Compositionality Prediction. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 20071–20092, Vienna, Austria. Association for Computational Linguistics.