Conceptualization topic modeling

@article{Tang2017ConceptualizationTM,
  title={Conceptualization topic modeling},
  author={Yi-Kun Tang and Xianling Mao and Heyan Huang and Xuewen Shi and Guihua Wen},
  journal={Multimedia Tools and Applications},
  year={2017},
  volume={77},
  pages={3455-3471}
}
  • Yi-Kun Tang, Xianling Mao, +2 authors Guihua Wen
  • Published in
    Multimedia Tools and…
    2017
  • Computer Science
  • 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… CONTINUE READING

    Create an AI-powered research feed to stay up to date with new papers like this posted to ArXiv

    6
    Twitter Mentions

    Citations

    Publications citing this paper.
    SHOWING 1-2 OF 2 CITATIONS

    Labeled Phrase Latent Dirichlet Allocation and its online learning algorithm

    VIEW 5 EXCERPTS
    CITES METHODS & BACKGROUND

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 31 REFERENCES

    Probase: a probabilistic taxonomy for text understanding

    VIEW 13 EXCERPTS
    HIGHLY INFLUENTIAL

    Latent Dirichlet Allocation

    VIEW 6 EXCERPTS
    HIGHLY INFLUENTIAL

    SSHLDA: A Semi-Supervised Hierarchical Topic Model

    VIEW 4 EXCERPTS

    Guided HTM: Hierarchical Topic Model with Dirichlet Forest Priors

    VIEW 1 EXCERPT