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    <titleInfo>
        <title>Definition generation for lexical semantic change detection</title>
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        <namePart type="given">Mariia</namePart>
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            <namePart type="given">Lun-Wei</namePart>
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            <namePart type="given">Andre</namePart>
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            <namePart type="given">Vivek</namePart>
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    <abstract>We use contextualized word definitions generated by large language models as semantic representations in the task of diachronic lexical semantic change detection (LSCD). In short, generated definitions are used as ‘senses’, and the change score of a target word is retrieved by comparing their distributions in two time periods under comparison. On the material of five datasets and three languages, we show that generated definitions are indeed specific and general enough to convey a signal sufficient to rank sets of words by the degree of their semantic change over time. Our approach is on par with or outperforms prior non-supervised sense-based LSCD methods. At the same time, it preserves interpretability and allows to inspect the reasons behind a specific shift in terms of discrete definitions-as-senses. This is another step in the direction of explainable semantic change modeling.</abstract>
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%0 Conference Proceedings
%T Definition generation for lexical semantic change detection
%A Fedorova, Mariia
%A Kutuzov, Andrey
%A Scherrer, Yves
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F fedorova-etal-2024-definition
%X We use contextualized word definitions generated by large language models as semantic representations in the task of diachronic lexical semantic change detection (LSCD). In short, generated definitions are used as ‘senses’, and the change score of a target word is retrieved by comparing their distributions in two time periods under comparison. On the material of five datasets and three languages, we show that generated definitions are indeed specific and general enough to convey a signal sufficient to rank sets of words by the degree of their semantic change over time. Our approach is on par with or outperforms prior non-supervised sense-based LSCD methods. At the same time, it preserves interpretability and allows to inspect the reasons behind a specific shift in terms of discrete definitions-as-senses. This is another step in the direction of explainable semantic change modeling.
%U https://aclanthology.org/2024.findings-acl.339
%P 5712-5724

Markdown (Informal)

[Definition generation for lexical semantic change detection](https://aclanthology.org/2024.findings-acl.339) (Fedorova et al., Findings 2024)

• Definition generation for lexical semantic change detection (Fedorova et al., Findings 2024)

ACL

• Mariia Fedorova, Andrey Kutuzov, and Yves Scherrer. 2024. Definition generation for lexical semantic change detection. In Findings of the Association for Computational Linguistics ACL 2024, pages 5712–5724, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.