@inproceedings{chen-etal-2021-tree,
title = "Tree-Structured Topic Modeling with Nonparametric Neural Variational Inference",
author = "Chen, Ziye and
Ding, Cheng and
Zhang, Zusheng and
Rao, Yanghui and
Xie, Haoran",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.182",
doi = "10.18653/v1/2021.acl-long.182",
pages = "2343--2353",
abstract = "Topic modeling has been widely used for discovering the latent semantic structure of documents, but most existing methods learn topics with a flat structure. Although probabilistic models can generate topic hierarchies by introducing nonparametric priors like Chinese restaurant process, such methods have data scalability issues. In this study, we develop a tree-structured topic model by leveraging nonparametric neural variational inference. Particularly, the latent components of the stick-breaking process are first learned for each document, then the affiliations of latent components are modeled by the dependency matrices between network layers. Utilizing this network structure, we can efficiently extract a tree-structured topic hierarchy with reasonable structure, low redundancy, and adaptable widths. Experiments on real-world datasets validate the effectiveness of our method.",
}
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<abstract>Topic modeling has been widely used for discovering the latent semantic structure of documents, but most existing methods learn topics with a flat structure. Although probabilistic models can generate topic hierarchies by introducing nonparametric priors like Chinese restaurant process, such methods have data scalability issues. In this study, we develop a tree-structured topic model by leveraging nonparametric neural variational inference. Particularly, the latent components of the stick-breaking process are first learned for each document, then the affiliations of latent components are modeled by the dependency matrices between network layers. Utilizing this network structure, we can efficiently extract a tree-structured topic hierarchy with reasonable structure, low redundancy, and adaptable widths. Experiments on real-world datasets validate the effectiveness of our method.</abstract>
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%0 Conference Proceedings
%T Tree-Structured Topic Modeling with Nonparametric Neural Variational Inference
%A Chen, Ziye
%A Ding, Cheng
%A Zhang, Zusheng
%A Rao, Yanghui
%A Xie, Haoran
%Y Zong, Chengqing
%Y Xia, Fei
%Y Li, Wenjie
%Y Navigli, Roberto
%S Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F chen-etal-2021-tree
%X Topic modeling has been widely used for discovering the latent semantic structure of documents, but most existing methods learn topics with a flat structure. Although probabilistic models can generate topic hierarchies by introducing nonparametric priors like Chinese restaurant process, such methods have data scalability issues. In this study, we develop a tree-structured topic model by leveraging nonparametric neural variational inference. Particularly, the latent components of the stick-breaking process are first learned for each document, then the affiliations of latent components are modeled by the dependency matrices between network layers. Utilizing this network structure, we can efficiently extract a tree-structured topic hierarchy with reasonable structure, low redundancy, and adaptable widths. Experiments on real-world datasets validate the effectiveness of our method.
%R 10.18653/v1/2021.acl-long.182
%U https://aclanthology.org/2021.acl-long.182
%U https://doi.org/10.18653/v1/2021.acl-long.182
%P 2343-2353
Markdown (Informal)
[Tree-Structured Topic Modeling with Nonparametric Neural Variational Inference](https://aclanthology.org/2021.acl-long.182) (Chen et al., ACL-IJCNLP 2021)
ACL
- Ziye Chen, Cheng Ding, Zusheng Zhang, Yanghui Rao, and Haoran Xie. 2021. Tree-Structured Topic Modeling with Nonparametric Neural Variational Inference. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 2343–2353, Online. Association for Computational Linguistics.