Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/101640
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dc.contributorDepartment of Chinese and Bilingual Studies-
dc.contributorDepartment of Computing-
dc.creatorGu, J-
dc.creatorXiang, R-
dc.creatorWang, X-
dc.creatorLi, J-
dc.creatorLi, W-
dc.creatorQian, L-
dc.creatorZhou, G-
dc.creatorHuang, CR-
dc.date.accessioned2023-09-18T07:35:20Z-
dc.date.available2023-09-18T07:35:20Z-
dc.identifier.urihttp://hdl.handle.net/10397/101640-
dc.language.isoenen_US
dc.publisherBioMed Centralen_US
dc.rights© The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.en_US
dc.rightsThe following publication Gu, J., Xiang, R., Wang, X., Li, J., Li, W., Qian, L., ... & Huang, C. R. (2022). Multi-probe attention neural network for COVID-19 semantic indexing. BMC bioinformatics, 23(1), 259 is available at https://doi.org/10.1186/s12859-022-04803-x.en_US
dc.subjectBiomedical semantic indexingen_US
dc.subjectCOVID-19en_US
dc.subjectDeep learningen_US
dc.subjectTopic identificationen_US
dc.titleMulti-probe attention neural network for COVID-19 semantic indexingen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume23en_US
dc.identifier.issue1en_US
dc.identifier.doi10.1186/s12859-022-04803-xen_US
dcterms.abstractBackground: The COVID-19 pandemic has increasingly accelerated the publication pace of scientific literature. How to efficiently curate and index this large amount of biomedical literature under the current crisis is of great importance. Previous literature indexing is mainly performed by human experts using Medical Subject Headings (MeSH), which is labor-intensive and time-consuming. Therefore, to alleviate the expensive time consumption and monetary cost, there is an urgent need for automatic semantic indexing technologies for the emerging COVID-19 domain.-
dcterms.abstractResults: In this research, to investigate the semantic indexing problem for COVID-19, we first construct the new COVID-19 Semantic Indexing dataset, which consists of more than 80 thousand biomedical articles. We then propose a novel semantic indexing framework based on the multi-probe attention neural network (MPANN) to address the COVID-19 semantic indexing problem. Specifically, we employ a k-nearest neighbour based MeSH masking approach to generate candidate topic terms for each input article. We encode and feed the selected candidate terms as well as other contextual information as probes into the downstream attention-based neural network. Each semantic probe carries specific aspects of biomedical knowledge and provides informatively discriminative features for the input article. After extracting the semantic features at both term-level and document-level through the attention-based neural network, MPANN adopts a linear multi-view classifier to conduct the final topic prediction for COVID-19 semantic indexing.-
dcterms.abstractConclusion: The experimental results suggest that MPANN promises to represent the semantic features of biomedical texts and is effective in predicting semantic topics for COVID-19 related biomedical articles.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationBMC Bioinformatics, 2022, v. 23, no. 1, 259en_US
dcterms.isPartOfBMC bioinformaticsen_US
dcterms.issued2022-
dc.identifier.scopus2-s2.0-85133104725-
dc.identifier.pmid35768777-
dc.identifier.eissn1471-2105en_US
dc.identifier.artn259en_US
dc.description.validate202309 bcvc-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_Scopus/WOS-
dc.description.fundingSourceRGCen_US
dc.description.fundingSourceOthersen_US
dc.description.fundingTextHong Kong Polytechnic University; National Natural Science Foundation of China; NSFC Young Scientists Fund; CCF-Tencent Rhino-Bird Young Faculty Open Research Funden_US
dc.description.pubStatusPublisheden_US
dc.description.oaCategoryCCen_US
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