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Conceptualization topic modeling

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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.

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Notes

  1. Source code can be found at https://github.com/anonymity01/CLDA

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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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Authors and Affiliations

  1. School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China

    Yi-Kun Tang, Xian-Ling Mao, Heyan Huang & Xuewen Shi

  2. Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, Minjiang University, Fuzhou, 350121, China

    Yi-Kun Tang

  3. Department of Computer Science and Technology, South China University of Technology, Guangzhou Shi, 510630, Guangdong Sheng, China

    Guihua Wen

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  1. Yi-Kun Tang
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  2. Xian-Ling Mao
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  5. Guihua Wen
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Correspondence to Xian-Ling Mao.

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Tang, YK., Mao, XL., Huang, H. et al. Conceptualization topic modeling. Multimed Tools Appl 77, 3455–3471 (2018). https://doi.org/10.1007/s11042-017-5145-4

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