Studying word meaning evolution through incremental semantic shift detection.
The study of semantic shift, that is, of how words change meaning as a consequence of social practices, events and political circumstances, is relevant in Natural Language Processing, Linguistics, and Social Sciences. The increasing availability of large diachronic corpora and advance in computation...
| Publicado en: | Language Resources & Evaluation Vol. 59; no. 2; pp. 1363 - 1400 |
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| Autores principales: | , , , , |
| Formato: | Artículo |
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Springer Nature
Jun2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=185240061&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 185240061 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 1574020X 179V jtl: Language Resources & Evaluation issn: 1574020X maglogo: N pubinfo: dt: Jun2025 vid: 59 iid: 2 pid: 237 pub: Springer Nature artinfo: ui: 185240061 10.1007/s10579-024-09769-1 ppf: 1363 ppct: 37 formats: fmt: – @attributes: type: T – @attributes: type: P size: 1.7MB tig: atl: Studying word meaning evolution through incremental semantic shift detection. aug: au: Periti, Francesco Picascia, Sergio Montanelli, Stefano Ferrara, Alfio Tahmasebi, Nina affil: https://ror.org/00wjc7c48 Department of Computer Science, University of Milan, Milan, Italy https://ror.org/01tm6cn81 Department of Philosophy, Linguistics and Theory of Science, University of Gothenburg, Gothenburg, Sweden su: Natural language processing Cognitive psychology Semantics Cognitive linguistics Social impact sug: subj: Natural language processing Cognitive psychology Semantics Cognitive linguistics Social impact keyword: Communication and Culture Linguistics Psychology and Cognitive Sciences Cognitive Sciences Contextualized word embeddings Evolutionary clustering Language Lexical semantic change Semantic shift detection ab: The study of semantic shift, that is, of how words change meaning as a consequence of social practices, events and political circumstances, is relevant in Natural Language Processing, Linguistics, and Social Sciences. The increasing availability of large diachronic corpora and advance in computational semantics have accelerated the development of computational approaches to detecting such shift. In this paper, we introduce a novel approach to tracing the evolution of word meaning over time. Our analysis focuses on gradual changes in word semantics and relies on an incremental approach to semantic shift detection (SSD) called What is Done is Done (WiDiD). WiDiD leverages scalable and evolutionary clustering of contextualised word embeddings to detect semantic shift and capture temporal transactions in word meanings. Existing approaches to SSD: (a) significantly simplify the semantic shift problem to cover change between two (or a few) time points, and (b) consider the existing corpora as static. We instead treat SSD as an organic process in which word meanings evolve across tens or even hundreds of time periods as the corpus is progressively made available. This results in an extremely demanding task that entails a multitude of intricate decisions. We demonstrate the applicability of this incremental approach on a diachronic corpus of Italian parliamentary speeches spanning eighteen distinct time periods. We also evaluate its performance on seven popular labelled benchmarks for SSD across multiple languages. Empirical results show that our results are comparable to state-of-the-art approaches, while outperforming the state-of-the-art for certain languages. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Language Resources & Evaluation is a copyright of Springer, 2025. All Rights Reserved. item: Language Resources & Evaluation holder: Springer Nature dt: @attributes: year: 2025 holdings: @attributes: islocal: N |
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