Discovering topic structures of a temporally evolving document corpus

Author:

Beykikhoshk Adham,Arandjelović Ognjen,Phung Dinh,Venkatesh Svetha

Funder

University of St. Andrews

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Hardware and Architecture,Human-Computer Interaction,Information Systems,Software

Reference68 articles.

1. American Psychiatric Association (2013) Autism spectrum disorder fact sheet. American Psychiatric Publishing, Arlington

2. Ahmed A, Xing EP (2012) Timeline: a dynamic hierarchical Dirichlet process model for recovering birth/death and evolution of topics in text stream. arXiv:1203.3463

3. Alberti KGMM, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, Fruchart J-C, James WPT, Loria CM, Smith SC Jr (2009) Harmonizing the metabolic syndrome. Circulation 120(16):1640–1645

4. Andrei V, Arandjelović O (2016) Complex temporal topic evolution modelling using the Kullback–Leibler divergence and the Bhattacharyya distance. EURASIP J Bioinf Syst Biol 1:1–11

5. Andrei V, Arandjelović O (2016) Identification of promising research directions using machine learning aided medical literature analysis. In: Proceedings of international conference of the IEEE engineering in medicine and biology society, pp 2471–2474

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