Complex temporal topic evolution modelling using the Kullback-Leibler divergence and the Bhattacharyya distance

Victor Andrei, Ognjen Arandelovic

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)

Abstract

The 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.
Original languageEnglish
Article number16
Number of pages11
JournalEURASIP Journal on Bioinformatics and Systems Biology
DOIs
Publication statusPublished - 29 Sept 2016

Keywords

  • Topic modelling
  • Dirichlet process
  • Bayesian
  • Temporal graph
  • Hierarchical model

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