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http://hdl.handle.net/10397/90388
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Chinese and Bilingual Studies | en_US |
| dc.contributor | Department of Computing | en_US |
| dc.creator | Chersoni, E | en_US |
| dc.creator | Xiang, R | en_US |
| dc.creator | Lu, Q | en_US |
| dc.creator | Huang, CR | en_US |
| dc.date.accessioned | 2021-06-28T07:25:45Z | - |
| dc.date.available | 2021-06-28T07:25:45Z | - |
| dc.identifier.uri | http://hdl.handle.net/10397/90388 | - |
| dc.language.iso | en | en_US |
| dc.rights | © 1963–2021 ACL | en_US |
| dc.rights | This work is licensed under a Creative Commons Attribution 4.0 International License. License details: http://creativecommons.org/licenses/by/4.0/. | en_US |
| dc.rights | The 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 https://www.aclweb.org/anthology/2020.starsem-1.4 | en_US |
| dc.title | Automatic learning of modality exclusivity norms with crosslingual word embeddings | en_US |
| dc.type | Conference Paper | en_US |
| dc.identifier.spage | 32 | en_US |
| dc.identifier.epage | 38 | en_US |
| dcterms.abstract | Collecting 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.abstract | In 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.accessRights | open access | en_US |
| dcterms.bibliographicCitation | Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics, Barcelona, Spain, December 2020, p. 32-38 | en_US |
| dcterms.issued | 2020-12 | - |
| dc.relation.ispartofbook | Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics | en_US |
| dc.description.validate | 202106 bcvc | en_US |
| dc.description.oa | Version of Record | en_US |
| dc.identifier.FolderNumber | a0670-n17 | - |
| dc.description.pubStatus | Published | en_US |
| Appears in Collections: | Conference Paper | |
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| File | Description | Size | Format | |
|---|---|---|---|---|
| 2020.starsem-1.4.pdf | 313.92 kB | Adobe PDF | View/Open |
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