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dc.contributor.authorAndrei, Victor
dc.contributor.authorArandelovic, Ognjen
dc.identifier.citationAndrei , V & Arandelovic , O 2016 , ' Complex temporal topic evolution modelling using the Kullback-Leibler divergence and the Bhattacharyya distance ' , EURASIP Journal on Bioinformatics and Systems Biology .
dc.identifier.otherPURE: 245601315
dc.identifier.otherPURE UUID: 1117dbe5-0e09-4d29-bdda-5af8223f9591
dc.identifier.otherScopus: 84990173953
dc.identifier.otherWOS: 000390999200001
dc.description.abstractThe rapidly expanding corpus of medical research literature presents major challenges in the understanding of previous work, the extraction of maximum information from collected data, and the identification of promising research directions. We present a case for the use of advanced machine learning techniques as an aide in this task and introduce a novel methodology that is shown to be capable of extracting meaningful information from large longitudinal corpora and of tracking complex temporal changes within it. Our framework is based on (i) the discretization of time into epochs, (ii) epoch-wise topic discovery using a hierarchical Dirichlet process-based model, and (iii) a temporal similarity graph which allows for the modelling of complex topic changes. More specifically, this is the first work that discusses and distinguishes between two groups of particularly challenging topic evolution phenomena: topic splitting and speciation and topic convergence and merging, in addition to the more widely recognized emergence and disappearance and gradual evolution. The proposed framework is evaluated on a public medical literature corpus.
dc.relation.ispartofEURASIP Journal on Bioinformatics and Systems Biologyen
dc.rights© The Author(s) 2016. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.en
dc.subjectTopic modellingen
dc.subjectDirichlet processen
dc.subjectTemporal graphen
dc.subjectHierarchical modelen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectQH301 Biologyen
dc.subjectR Medicineen
dc.titleComplex temporal topic evolution modelling using the Kullback-Leibler divergence and the Bhattacharyya distanceen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews. School of Computer Scienceen
dc.description.statusPeer revieweden

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