Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/97883
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Chinese and Bilingual Studies | en_US |
dc.creator | Gu, J | en_US |
dc.creator | Wang, X | en_US |
dc.creator | Chersoni, E | en_US |
dc.creator | Huang, CR | en_US |
dc.date.accessioned | 2023-03-24T07:39:49Z | - |
dc.date.available | 2023-03-24T07:39:49Z | - |
dc.identifier.isbn | 978-0-578-32368-8 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/97883 | - |
dc.language.iso | en | en_US |
dc.rights | Posted with permission of the publisher. | en_US |
dc.rights | The 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.subject | COVID-19 | en_US |
dc.subject | LitCovid | en_US |
dc.subject | Pre-trained model | en_US |
dc.subject | Deep learning | en_US |
dc.subject | Multilabel classification | en_US |
dc.subject | Ensemble learning | en_US |
dc.title | Team PolyU-CBSNLP at BioCreative-VII LitCovid track : ensemble learning for COVID-19 multilabel classification | en_US |
dc.type | Conference Paper | en_US |
dc.identifier.spage | 326 | en_US |
dc.identifier.epage | 331 | en_US |
dcterms.abstract | This 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.accessRights | open access | en_US |
dcterms.bibliographicCitation | In Proceedings of the BioCreative VII Challenge Evaluation Workshop, November 08-10, 2021, Virtual, p. 326-331 | en_US |
dcterms.issued | 2021 | - |
dc.relation.ispartofbook | Proceedings of the BioCreative VII Challenge Evaluation Workshop, November 08-10, 2021, Virtual | en_US |
dc.relation.conference | BioCreative | en_US |
dc.description.validate | 202303 bcww | en_US |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | CBS-0077 | - |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | This work is supported by the research grants of The Hong Kong Polytechnic University Projects #G-YW4H and #1-W182 | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 57916056 | - |
dc.description.oaCategory | Publisher permission | en_US |
Appears in Collections: | Conference Paper |
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File | Description | Size | Format | |
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TRACK5_pos_16_BC7_submission_211.pdf | 305.51 kB | Adobe PDF | View/Open |
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