Hierarchical neural topic modeling with manifold regularization

Ziye CHEN, Cheng DING, Yanghui RAO*, Haoran XIE, Xiaohui TAO, Gary CHENG, Fu Lee WANG

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-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.
Original languageEnglish
Pages (from-to)2139-2160
Number of pages22
JournalWorld Wide Web
Issue number6
Early online date15 Oct 2021
Publication statusPublished - Nov 2021

Bibliographical note

Funding Information:
We are grateful to the reviewers for their constructive comments and suggestions on this study. This work has been supported in part by the National Natural Science Foundation of China (61972426), Guangdong Basic and Applied Basic Research Foundation (2020A1515010536), the Faculty Research Grants (DB21B6 and DB21A9) of Lingnan University, Hong Kong, and Research Grants Council of Hong Kong SAR, China (UGC/FDS16/E01/19). The work has also been supported in part by the One-off Special Fund from Central and Faculty Fund in Support of Research from 2019/20 to 2021/22 (MIT02/19-20), the Research Cluster Fund (RG 78/2019-2020R), The Education University of Hong Kong.

Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.


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


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