Abstract
In many domains, document sets are hierarchically organized such as message forums having multiple levels of sections. Analysis of latent topics within such content is crucial for tasks like trend and user interest analysis. Nonparametric topic models are a powerful approach, but traditional Hierarchical Dirichlet Processes (HDPs) are unable to fully take into account topic sharing across deep hierarchical structure. We propose the Tree-structured Hierarchical Dirichlet Process, allowing Dirichlet process based topic modeling over a given tree structure of arbitrary size and height, where documents can arise at all tree nodes. Experiments on a hierarchical social message forum and a product reviews forum demonstrate better generalization performance than traditional HDPs in terms of ability to model new data and classify documents to sections.
Md. H. Alam and J. Peltonen had equal contributions. The work was supported by Academy of Finland decisions 295694 and 313748.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Blei, D., Ng, A., Jordan, M.: Latent Dirichlet allocation. JMLR 3, 993–1022 (2003)
Teh, Y., Jordan, M., Beal, M., Blei, D.: Hierarchical Dirichlet processes. J. Am. Stat. Assoc. 101, 1566–1581 (2006)
Blei, D., Griffiths, T., Jordan, M.: The nested Chinese restaurant process and Bayesian nonparametric inference of topic hierarchies. J. ACM 57, 7:1–7:30 (2010)
Li, W., McCallum, A.: Pachinko allocation: DAG-structured mixture models of topic correlations. In: Proceedings of ICML, pp. 577–584. ACM (2006)
Adams, R., Ghahramani, Z., Jordan, M.: Tree-structured stick breaking for hierarchical data. In: Proceedings of NIPS, pp. 19–27. Curran Associates Inc. (2010)
Faisal, A., Gillberg, J., Leen, G., Peltonen, J.: Transfer learning using a nonparametric sparse topic model. Neurocomputing 112, 124–137 (2013)
Xu, Y., Yin, J., Huang, J., Yin, Y.: Hierarchical topic modeling with automatic knowledge mining. Expert Syst. Appl. 103, 106–117 (2018)
Kim, J., Kim, D., Kim, S., Oh, A.: Modeling topic hierarchies with the recursive Chinese restaurant process. In: Proceedings of CIKM, pp. 783–792. ACM (2012)
He, R., McAuley, J.: Ups and downs: modeling the visual evolution of fashion trends with one-class collaborative filtering. In: Proceedings of WWW, pp. 507–517 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Alam, M.H., Peltonen, J., Nummenmaa, J., Järvelin, K. (2019). Tree-Structured Hierarchical Dirichlet Process. In: Rodríguez, S., et al. Distributed Computing and Artificial Intelligence, Special Sessions, 15th International Conference. DCAI 2018. Advances in Intelligent Systems and Computing, vol 801. Springer, Cham. https://doi.org/10.1007/978-3-319-99608-0_33
Download citation
DOI: https://doi.org/10.1007/978-3-319-99608-0_33
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-99607-3
Online ISBN: 978-3-319-99608-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)