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dc.contributor.authorWu, Shaopeng
dc.contributor.authorZhao, Youbing
dc.contributor.authorParvinzamir, Farzad
dc.contributor.authorErsotelos, Nikolaos Th
dc.contributor.authorWei, Hui
dc.contributor.authorDong, Feng
dc.date.accessioned2019-09-26T14:20:44Z
dc.date.available2019-09-26T14:20:44Z
dc.date.issued2019-08-02
dc.identifier.citationWu, S., Zhao, Y., Parvinzamir, F. et al. (2019) Literature Explorer: effective retrieval of scientific documents through nonparametric thematic topic detection, The Visual Computer (2019), 36, pp. 1337–1354. https://doi.org/10.1007/s00371-019-01721-7en
dc.identifier.issn0178-2789en
dc.identifier.doi10.1007/s00371-019-01721-7en
dc.identifier.urihttp://hdl.handle.net/2436/622735
dc.description© 2020 The Authors. Published by Springer. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.1007/s00371-019-01721-7
dc.description.abstractScientific researchers are facing a rapidly growing volume of literatures nowadays. While these publications offer rich and valuable information, the scale of the datasets makes it difficult for the researchers to manage and search for desired information efficiently. Literature Explorer is a new interactive visual analytics suite that facilitates the access to desired scientific literatures through mining and interactive visualisation. We propose a novel topic mining method that is able to uncover “thematic topics” from a scientific corpus. These thematic topics have an explicit semantic association to the research themes that are commonly used by human researchers in scientific fields, and hence are human interpretable. They also contribute to effective document retrieval. The visual analytics suite consists of a set of visual components that are closely coupled with the underlying thematic topic detection to support interactive document retrieval. The visual components are adequately integrated under the design rationale and goals. Evaluation results are given in both objective measurements and subjective terms through expert assessments. Comparisons are also made against the outcomes from the traditional topic modelling methods.en
dc.description.sponsorshipThis research is supported by the European Commission with project Dr Inventor (No 611383), MyHealthAvatar (No 60929), and by the UK Engineering and Physical Sciences Research Council with project MyLifeHub (EP/L023830/1).en
dc.formatapplication/PDFen
dc.languageen
dc.language.isoenen
dc.publisherSpringer Science and Business Media LLCen
dc.relation.urlhttps://link.springer.com/article/10.1007%2Fs00371-019-01721-7en
dc.subjecttopic exploreren
dc.subjectdata visualisationen
dc.subjecttopic modellingen
dc.subjectText miningen
dc.subjectWeb applicationen
dc.subjectscientific documentsen
dc.titleLiterature Explorer: effective retrieval of scientific documents through nonparametric thematic topic detectionen
dc.typeJournal articleen
dc.identifier.eissn1432-2315
dc.identifier.journalThe Visual Computeren
dc.date.updated2019-09-17T10:31:25Z
dc.date.accepted2019-07-02
rioxxterms.funderJiscen
rioxxterms.identifier.projectEP/L023830/1en
rioxxterms.versionVoRen
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0/en
rioxxterms.licenseref.startdate2019-09-26en
dc.source.volume36
dc.source.beginpage1337
dc.source.endpage1354
dc.description.versionPublished online
refterms.dateFCD2019-09-26T14:20:29Z
refterms.versionFCDVoR
refterms.dateFOA2019-09-26T14:20:45Z


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