Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/97883
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dc.contributorDepartment of Chinese and Bilingual Studiesen_US
dc.creatorGu, Jen_US
dc.creatorWang, Xen_US
dc.creatorChersoni, Een_US
dc.creatorHuang, CRen_US
dc.date.accessioned2023-03-24T07:39:49Z-
dc.date.available2023-03-24T07:39:49Z-
dc.identifier.isbn978-0-578-32368-8en_US
dc.identifier.urihttp://hdl.handle.net/10397/97883-
dc.language.isoenen_US
dc.rightsPosted with permission of the publisher.en_US
dc.rightsThe following publication Gu, J., Wang, X., Chersoni, E., & Huang, C. R. (2021). Team polyU-CBSNLP at BioCreative-VII LitCovid Track: ensemble learning for COVID-19 multilabel classification. In Proceedings of the BioCreative VII Challenge Evaluation Workshop (p. 326-331) is available at https://biocreative.bioinformatics.udel.edu/resources/publications/bc-vii-workshop-proceedings/.en_US
dc.subjectCOVID-19en_US
dc.subjectLitCoviden_US
dc.subjectPre-trained modelen_US
dc.subjectDeep learningen_US
dc.subjectMultilabel classificationen_US
dc.subjectEnsemble learningen_US
dc.titleTeam PolyU-CBSNLP at BioCreative-VII LitCovid track : ensemble learning for COVID-19 multilabel classificationen_US
dc.typeConference Paperen_US
dc.identifier.spage326en_US
dc.identifier.epage331en_US
dcterms.abstractThis paper briefly describes our works for the LitCovid shared task of BioCreative-VII Track 5. It is an ensemble learning-based system that utilized multiple biomedical pretrained models. In particular, we leveraged seven advanced models for initialization with homogeneous and heterogenous structures through an ensemble bagging manner. To enhance the representation abilities, we further proposed to employ additional biomedical knowledge to facilitate ensemble learning. The experimental results on the LitCovid datasets show the effectiveness of our proposed approach.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationIn Proceedings of the BioCreative VII Challenge Evaluation Workshop, November 08-10, 2021, Virtual, p. 326-331en_US
dcterms.issued2021-
dc.relation.ispartofbookProceedings of the BioCreative VII Challenge Evaluation Workshop, November 08-10, 2021, Virtualen_US
dc.relation.conferenceBioCreativeen_US
dc.description.validate202303 bcwwen_US
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberCBS-0077-
dc.description.fundingSourceOthersen_US
dc.description.fundingTextThis work is supported by the research grants of The Hong Kong Polytechnic University Projects #G-YW4H and #1-W182en_US
dc.description.pubStatusPublisheden_US
dc.identifier.OPUS57916056-
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