Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/106693
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dc.contributorDepartment of Chinese and Bilingual Studies-
dc.creatorChen, J-
dc.creatorChersoni, E-
dc.creatorSchlechtweg, D-
dc.creatorProkic, J-
dc.creatorHuang, CR-
dc.date.accessioned2024-06-03T02:11:33Z-
dc.date.available2024-06-03T02:11:33Z-
dc.identifier.isbn979-8-89176-043-1-
dc.identifier.urihttp://hdl.handle.net/10397/106693-
dc.description4th International Workshop on Computational Approaches to Historical Language Change 2023, December 6, 2023, Singaporeen_US
dc.language.isoenen_US
dc.publisherAssociation for Computational Linguisticsen_US
dc.rights©2023 Association for Computational Linguisticsen_US
dc.rightsACL materials are Copyright © 1963–2024 ACL; other materials are copyrighted by their respective copyright holders. Materials prior to 2016 here are licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 International License (https://creativecommons.org/licenses/by-nc-sa/3.0/). Permission is granted to make copies for the purposes of teaching and research. Materials published in or after 2016 are licensed on a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).en_US
dc.rightsThe following publication Jing Chen, Emmanuele Chersoni, Dominik Schlechtweg, Jelena Prokic, and Chu-Ren Huang. 2023. ChiWUG: A Graph-based Evaluation Dataset for Chinese Lexical Semantic Change Detection. In Proceedings of the 4th Workshop on Computational Approaches to Historical Language Change, pages 93–99, Singapore. Association for Computational Linguistics is available at https://doi.org/10.18653/v1/2023.lchange-1.10.en_US
dc.titleChiWUG : a graph-based evaluation dataset for Chinese lexical semantic change detectionen_US
dc.typeConference Paperen_US
dc.identifier.spage93-
dc.identifier.epage99-
dc.identifier.doi10.18653/v1/2023.lchange-1.10-
dcterms.abstractRecent studies suggested that language models are efficient tools for measuring lexical semantic change. In our paper, we present the compilation of the first graph-based evaluation dataset for lexical semantic change in the context of the Chinese language, specifically covering the periods of pre- and post- Reform and Opening Up. Exploiting the existing framework DURel, we collect over 61,000 human semantic relatedness judgments for 40 targets. The inferred word usage graphs and semantic change scores provide a basis for visualization and evaluation of semantic change.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationIn 4th International Workshop on Computational Approaches to Historical Language Change 2023 : Proceedings of the Workshop, December 6, 2023, p. 93-99. Stroudsburg : Association for Computational Linguistics, 2023-
dcterms.issued2023-
dc.relation.ispartofbook4th International Workshop on Computational Approaches to Historical Language Change 2023 : Proceedings of the Workshop, December 6, 2023-
dc.relation.conferenceInternational Workshop on Computational Approaches to Historical Language Change [LChange]-
dc.description.validate202405 bcch-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumbera2727aen_US
dc.identifier.SubFormID48138en_US
dc.description.fundingSourceSelf-fundeden_US
dc.description.pubStatusPublisheden_US
dc.description.oaCategoryCCen_US
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