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Multimedia Tools and Applications

, Volume 77, Issue 3, pp 3455–3471 | Cite as

Conceptualization topic modeling

  • Yi-Kun Tang
  • Xian-Ling Mao
  • Heyan Huang
  • Xuewen Shi
  • Guihua Wen
Article

Abstract

Recently, topic modeling has been widely used to discover the abstract topics in the multimedia field. Most of the existing topic models are based on the assumption of three-layer hierarchical Bayesian structure, i.e. each document is modeled as a probability distribution over topics, and each topic is a probability distribution over words. However, the assumption is not optimal. Intuitively, it’s more reasonable to assume that each topic is a probability distribution over concepts, and then each concept is a probability distribution over words, i.e. adding a latent concept layer between topic layer and word layer in traditional three-layer assumption. In this paper, we verify the proposed assumption by incorporating the new assumption in two representative topic models, and obtain two novel topic models. Extensive experiments were conducted among the proposed models and corresponding baselines, and the results show that the proposed models significantly outperform the baselines in terms of case study and perplexity, which means the new assumption is more reasonable than traditional one.

Keywords

Conceptualization topic modeling Hierarchical bayesian structure Conceptualization latent dirichlet allocation Conceptualization labeled latent dirichlet allocation 

Notes

Acknowledgements

This work was supported by 863 Program (2015AA015404), China National Science Foundation (61402036, 60973083, 61273363), Beijing Technology Project (Z151100001615029), Science and Technology Planning Project of Guangdong Province (2014A010103009, 2015A020217002), Guangzhou Science and Technology Planning Project(201604020179). Open Fund Project of Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University) (No. MJUKF201738)

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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Yi-Kun Tang
    • 1
    • 2
  • Xian-Ling Mao
    • 1
  • Heyan Huang
    • 1
  • Xuewen Shi
    • 1
  • Guihua Wen
    • 3
  1. 1.School of Computer Science and TechnologyBeijing Institute of TechnologyBeijingChina
  2. 2.Fujian Provincial Key Laboratory of Information Processing and Intelligent ControlMinjiang UniversityFuzhouChina
  3. 3.Department of Computer Science and TechnologySouth China University of TechnologyGuangzhou ShiChina

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