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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.
|Number of pages||22|
|Journal||World Wide Web|
|Early online date||15 Oct 2021|
|Publication status||Published - Nov 2021|
Bibliographical noteFunding 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.
© 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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- 2 Finished
A Label Extension Schema for Improved Text Emotion Classification
1/07/21 → 30/06/22
Project: Grant Research
Facilitate Tree-Structured Topic Modeling via Nonparametric Neural Inference
1/03/21 → 28/02/22
Project: Grant Research