Stability of topic modeling via matrix factorization

DC FieldValueLanguage
dc.contributor.authorBelford, Mark-
dc.contributor.authorMacNamee, Brian-
dc.contributor.authorGreene, Derek-
dc.date.accessioned2018-01-24T11:58:46Z-
dc.date.copyright2018 Elsevieren
dc.date.issued2017-01-
dc.identifier.citationExpert Systems with Applicationsen
dc.identifier.urihttp://hdl.handle.net/10197/9198-
dc.description.abstractTopic models can provide us with an insight into the underlying latent structure of a large corpus of documents. A range of methodshave been proposed in the literature, including probabilistic topic models and techniques based on matrix factorization. However, inboth cases, standard implementations rely on stochastic elements in their initialization phase, which can potentially lead to differentresults being generated on the same corpus when using the same parameter values. This corresponds to the concept of instabilitywhich has previously been studied in the context of k-means clustering. In many applications of topic modeling, this problem ofinstability is not considered and topic models are treated as being definitive, even though the results may change considerably if theinitialization process is altered. In this paper we demonstrate the inherent instability of popular topic modeling approaches, usinga number of new measures to assess stability. To address this issue in the context of matrix factorization for topic modeling, wepropose the use of ensemble learning strategies. Based on experiments performed on annotated text corpora, we show that a K-Foldensemble strategy, combining both ensembles and structured initialization, can significantly reduce instability, while simultaneouslyyielding more accurate topic modelsen
dc.description.sponsorshipScience Foundation Irelanden
dc.language.isoenen
dc.publisherElsevieren
dc.rightsThis is the author’s version of a work that was accepted for publication in Expert Systems with Applications. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Expert Systems with Applications (98, (2018)) DOI:10.1016/j.eswa.2017.08.047en
dc.subjectTopic modelingen
dc.subjectTopic stabilityen
dc.subjectLDAen
dc.subjectNMFen
dc.titleStability of topic modeling via matrix factorizationen
dc.typeJournal Articleen
dc.statusPeer revieweden
dc.identifier.volume91en
dc.identifier.startpage159en
dc.identifier.endpage169en
dc.identifier.doi10.1016/j.eswa.2017.08.047-
dc.neeo.contributorBelford|Mark|aut|-
dc.neeo.contributorMacNamee|Brian|aut|-
dc.neeo.contributorGreene|Derek|aut|-
dc.date.embargo2019-09-01-
dc.date.updated2017-09-08T09:58:49Z-
dc.rights.licensehttps://creativecommons.org/licenses/by-nc-nd/3.0/ie/en
item.grantfulltextopen-
item.fulltextWith Fulltext-
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