DeepMeSH

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorPeng, Shengwen
dc.contributor.authorYou, Ronghui
dc.contributor.authorWang, Hongning
dc.contributor.authorZhai, Chengxiang
dc.contributor.authorMamitsuka, Hiroshi
dc.contributor.authorZhu, Shanfeng
dc.contributor.departmentFudan University
dc.contributor.departmentUniversity of Virginia
dc.contributor.departmentUniversity of Illinois at Urbana-Champaign
dc.contributor.departmentDepartment of Computer Science
dc.date.accessioned2016-10-13T06:15:43Z
dc.date.issued2016-06-15
dc.description.abstractMotivation: Medical Subject Headings (MeSH) indexing, which is to assign a set of MeSH main headings to citations, is crucial for many important tasks in biomedical text mining and information retrieval. Large-scale MeSH indexing has two challenging aspects: the citation side and MeSH side. For the citation side, all existing methods, including Medical Text Indexer (MTI) by National Library of Medicine and the state-of-the-art method, MeSHLabeler, deal with text by bag-of-words, which cannot capture semantic and context-dependent information well. Methods: We propose DeepMeSH that incorporates deep semantic information for large-scale MeSH indexing. It addresses the two challenges in both citation and MeSH sides. The citation side challenge is solved by a new deep semantic representation, D2V-TFIDF, which concatenates both sparse and dense semantic representations. The MeSH side challenge is solved by using the 'learning to rank' framework of MeSHLabeler, which integrates various types of evidence generated from the new semantic representation. Results: DeepMeSH achieved a Micro F-measure of 0.6323, 2% higher than 0.6218 of MeSHLabeler and 12% higher than 0.5637 of MTI, for BioASQ3 challenge data with 6000 citations.en
dc.description.versionPeer revieweden
dc.format.extent10
dc.format.extenti70-i79
dc.format.mimetypeapplication/pdf
dc.identifier.citationPeng , S , You , R , Wang , H , Zhai , C , Mamitsuka , H & Zhu , S 2016 , ' DeepMeSH : Deep semantic representation for improving large-scale MeSH indexing ' , Bioinformatics , vol. 32 , no. 12 , pp. i70-i79 . https://doi.org/10.1093/bioinformatics/btw294en
dc.identifier.doi10.1093/bioinformatics/btw294
dc.identifier.issn1367-4803
dc.identifier.issn1460-2059
dc.identifier.otherPURE UUID: cd1de4e7-fb1f-41e1-aaed-838a21396f46
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/cd1de4e7-fb1f-41e1-aaed-838a21396f46
dc.identifier.otherPURE LINK: http://www.scopus.com/inward/record.url?scp=84976502747&partnerID=8YFLogxK
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/6483933/i70.full.pdf
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/22952
dc.identifier.urnURN:NBN:fi:aalto-201610135052
dc.language.isoenen
dc.relation.ispartofseriesBIOINFORMATICSen
dc.relation.ispartofseriesVolume 32, issue 12en
dc.rightsopenAccessen
dc.subject.keywordbiomedical text mining
dc.subject.keywordOntology
dc.subject.keywordtext mining
dc.subject.keywordMeSH
dc.subject.keyworddeep learning
dc.titleDeepMeSHen
dc.typeA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessäfi
dc.type.versionpublishedVersion
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