Uncertainty in Artificial Intelligence
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Integrating Document Clustering and Topic Modeling
Pengtao Xie, Eric Xing
Abstract:
Document clustering and topic modeling are two closely related tasks which can mutually benefit each other. Topic modeling can project documents into a topic space which facilitates effective document clustering. Cluster labels discovered by document clustering can be incorporated into topic models to extract local topics specific to each cluster and global topics shared by all clusters. In this paper, we propose a multi-grain clustering topic model (MGCTM) which integrates document clustering and topic modeling into a unified framework and jointly performs the two tasks to achieve the overall best performance. Our model tightly couples two components: a mixture component used for discovering latent groups in document collection and a topic model component used for mining multi-grain topics including local topics specific to each cluster and global topics shared across clusters.We employ variational inference to approximate the posterior of hidden variables and learn model parameters. Experiments on two datasets demonstrate the effectiveness of our model.
Keywords:
Pages: 694-703
PS Link:
PDF Link: /papers/13/p694-xie.pdf
BibTex:
@INPROCEEDINGS{Xie13,
AUTHOR = "Pengtao Xie and Eric Xing",
TITLE = "Integrating Document Clustering and Topic Modeling",
BOOKTITLE = "Proceedings of the Twenty-Ninth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-13)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2013",
PAGES = "694--703"
}


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