Sense through time: diachronic word sense annotations for word sense induction and Lexical Semantic Change Detection.
There has been extensive work on human word sense annotation, i.e., manually labeling word uses in natural texts according to their senses. Such labels were primarily created for the tasks of Word Sense Disambiguation (WSD) and Word Sense Induction (WSI). However, almost all datasets annotated with...
| Publicado en: | Language Resources & Evaluation Vol. 59; no. 2; pp. 1431 - 1466 |
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| Autores principales: | , , , |
| Formato: | Artículo |
| Publicado: |
Springer Nature
Jun2025
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| Materias: | |
| 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=185240063&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 185240063 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: 185240063 10.1007/s10579-024-09771-7 ppf: 1431 ppct: 35 formats: fmt: – @attributes: type: T – @attributes: type: P size: 2.4MB tig: atl: Sense through time: diachronic word sense annotations for word sense induction and Lexical Semantic Change Detection. aug: au: Schlechtweg, Dominik Zamora-Reina, Frank D. Bravo-Marquez, Felipe Arefyev, Nikolay affil: https://ror.org/04vnq7t77 Institute for Natural Language Processing, University of Stuttgart, Stuttgart, Germany https://ror.org/047gc3g35 Department of Computer Science, CENIA & IMFD, University of Chile, Santiago, Chile https://ror.org/01xtthb56 Department of Informatics, University of Oslo, Oslo, Norway su: Senses Annotations Vocabulary sug: subj: Senses Annotations Vocabulary keyword: Diachronic Historical Lexical semantic change detection Word sense disambiguation Word sense induction Word-in-context ab: There has been extensive work on human word sense annotation, i.e., manually labeling word uses in natural texts according to their senses. Such labels were primarily created for the tasks of Word Sense Disambiguation (WSD) and Word Sense Induction (WSI). However, almost all datasets annotated with word senses are synchronic datasets, i.e., contain texts created in a relatively short period of time and often do not provide the creation date of the texts. This ignores possible applications in diachronic-historic settings, where the aim is to induce or disambiguate historical word senses or changes in senses across time. To facilitate investigations into historical WSD and WSI and to establish connections with the task of Lexical Semantic Change Detection (LSCD), there is a crucial need for historical word sense-annotated data. Hence, we created a new reliable diachronic WSD/WSI dataset 'DWUG DE Sense'. We describe the preparation and annotation and analyze central statistics. We then describe a thorough evaluation of different prediction systems for jointly solving both WSI and LSCD tasks. All our systems are based on a state-of-the-art architecture that combines Word-in-Context models and graph clustering techniques with different hyperparameter settings. Our findings reveal that using the WSI task as optimization criterion yields better results for both tasks even when the LSCD task is the focal point of optimization. This underscores that although both tasks are related, WSI seems to be more general and able to incorporate the LSCD task. 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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