Hierarchical neural topic modeling with manifold regularization

Ziye CHEN, Cheng DING, Yanghui RAO, Haoran XIE, Xiaohui TAO, Kwok Shing CHENG, Fu Lee WANG

Research output: Contribution to journalArticlespeer-review

6 Citations (Scopus)


Topic models have been widely used for learning the latent explainable representation of documents, but most of the existing approaches discover topics in a flat structure. In this study, we propose an effective hierarchical neural topic model with strong interpretability. Unlike the previous neural topic models, we explicitly model the dependency between layers of a network, and then combine latent variables of different layers to reconstruct documents. Utilizing this network structure, our model can extract a tree-shaped topic hierarchy with low redundancy and good explainability by exploiting dependency matrices. Furthermore, we introduce manifold regularization into the proposed method to improve the robustness of topic modeling. Experiments on real-world datasets validate that our model outperforms other topic models in several widely used metrics with much fewer computation costs. Copyright © 2021 The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Original languageEnglish
Pages (from-to)2139-2160
JournalWorld Wide Web
Issue number6
Early online date15 Oct 2021
Publication statusPublished - Nov 2021


Chen, Z., Ding, C., Rao, Y., Xie, H., Tao, X., Cheng, G., & Wang, F. L. (2021). Hierarchical neural topic modeling with manifold regularization. World Wide Web, 24(6), 2139-2160. doi: 10.1007/s11280-021-00963-7


  • Neural topic modeling
  • Hierarchical structure
  • Tree network
  • Manifold regularization


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