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

Overview of attention for article published in EURASIP Journal on Bioinformatics & Systems Biology, September 2016
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Title
Complex temporal topic evolution modelling using the Kullback-Leibler divergence and the Bhattacharyya distance
Published in
EURASIP Journal on Bioinformatics & Systems Biology, September 2016
DOI 10.1186/s13637-016-0050-0
Pubmed ID
Authors

Victor Andrei, Ognjen Arandjelović

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.

Mendeley readers

The data shown below were compiled from readership statistics for 6 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 6 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 1 17%
Student > Ph. D. Student 1 17%
Researcher 1 17%
Student > Doctoral Student 1 17%
Professor > Associate Professor 1 17%
Other 1 17%
Readers by discipline Count As %
Computer Science 3 50%
Biochemistry, Genetics and Molecular Biology 1 17%
Unspecified 1 17%
Engineering 1 17%