Definition generation for lexical semantic change detection

• URL: http://arxiv.org/abs/2406.14167v2
• Date: Wed, 31 Jul 2024 16:20:45 GMT
• Title: Definition generation for lexical semantic change detection
• Authors: Mariia Fedorova, Andrey Kutuzov, Yves Scherrer,
• Abstract summary: 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. Our approach is on par with or outperforms prior non-supervised LSCD methods.
• Score: 3.7297237438000788
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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