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dc.contributor.authorBeykikhoshk, Adham
dc.contributor.authorArandelovic, Ognjen
dc.contributor.authorPhung, Dinh
dc.contributor.authorVenkatesh, Svetha
dc.date.accessioned2017-08-11T11:30:25Z
dc.date.available2017-08-11T11:30:25Z
dc.date.issued2018-06
dc.identifier.citationBeykikhoshk , A , Arandelovic , O , Phung , D & Venkatesh , S 2018 , ' Discovering topic structures of a temporally evolving document corpus ' Knowledge and Information Systems , vol. 55 , no. 3 , pp. 599-632 . https://doi.org/10.1007/s10115-017-1095-4en
dc.identifier.issn0219-1377
dc.identifier.otherPURE: 250119430
dc.identifier.otherPURE UUID: 27b70ebc-36a9-4e74-86d2-27099560ed8f
dc.identifier.otherScopus: 85027159488
dc.identifier.urihttp://hdl.handle.net/10023/11428
dc.description.abstractIn this paper we describe a novel framework for the discovery of the topical content of a data corpus, and the tracking of its complex structural changes across the temporal dimension. In contrast to previous work our model does not impose a prior on the rate at which documents are added to the corpus nor does it adopt the Markovian assumption which overly restricts the type of changes that the model can capture. Our key technical contribution is a framework based on (i) 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: emergence and disappearance, evolution, splitting, and merging. The power of the proposed framework is demonstrated on two medical literature corpora concerned with the autism spectrum disorder (ASD) and the metabolic syndrome (MetS)—both increasingly important research subjects with significant social and healthcare consequences. In addition to the collected ASD and metabolic syndrome literature corpora which we made freely available, our contribution also includes an extensive empirical analysis of the proposed framework. We describe a detailed and careful examination of the effects that our algorithms’s free parameters have on its output and discuss the significance of the findings both in the context of the practical application of our algorithm as well as in the context of the existing body of work on temporal topic analysis. Our quantitative analysis is followed by several qualitative case studies highly relevant to the current research on ASD and MetS, on which our algorithm is shown to capture well the actual developments in these fields.en
dc.format.extent34en
dc.language.isoeng
dc.relation.ispartofKnowledge and Information Systemsen
dc.rights© The Author(s) 2017. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), 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.subjectData miningen
dc.subjectNon-parametricen
dc.subjectBayesianen
dc.subjectAutismen
dc.subjectASDen
dc.subjectMetabolic syndromeen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectRC0321 Neuroscience. Biological psychiatry. Neuropsychiatryen
dc.subjectNDASen
dc.subject.lccQA75en
dc.subject.lccRC0321en
dc.titleDiscovering topic structures of a temporally evolving document corpusen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews. School of Computer Scienceen
dc.identifier.doihttps://doi.org/10.1007/s10115-017-1095-4
dc.description.statusPeer revieweden
dc.date.embargoedUntil10-08-20


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