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dc.contributorDepartment of Chinese and Bilingual Studiesen_US
dc.contributorDepartment of Computingen_US
dc.creatorChersoni, Een_US
dc.creatorXiang, Ren_US
dc.creatorLu, Qen_US
dc.creatorHuang, CRen_US
dc.rights© 1963–2021 ACLen_US
dc.rightsThis work is licensed under a Creative Commons Attribution 4.0 International License. License details:
dc.rightsThe following publication Chersoni, E., Xiang, R., Lu, Q., & Huang, C. R. (2020, December). Automatic learning of modality exclusivity norms with crosslingual word embeddings. In Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics (pp. 32-38) is available at
dc.titleAutomatic learning of modality exclusivity norms with crosslingual word embeddingsen_US
dc.typeConference Paperen_US
dcterms.abstractCollecting modality exclusivity norms for lexical items has recently become a common practice in psycholinguistics and cognitive research. However, these norms are available only for a relatively small number of languages and often involve a costly and time-consuming collection of ratings.en_US
dcterms.abstractIn this work, we aim at learning a mapping between word embeddings and modality norms. Our experiments focused on crosslingual word embeddings, in order to predict modality association scores by training on a high-resource language and testing on a low-resource one. We ran two experiments, one in a monolingual and the other one in a crosslingual setting. Results show that modality prediction using off-the-shelf crosslingual embeddings indeed has moderate-to-high correlations with human ratings even when regression algorithms are trained on an English resource and tested on a completely unseen language.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationProceedings of the Ninth Joint Conference on Lexical and Computational Semantics, Barcelona, Spain, December 2020, p. 32-38en_US
dc.relation.ispartofbookProceedings of the Ninth Joint Conference on Lexical and Computational Semanticsen_US
dc.description.validate202106 bcvcen_US
dc.description.oaVersion of Recorden_US
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