2013 OneThemeinallViewsModelingConse

• (Tang et al., 2013) ⇒ Jian Tang, Ming Zhang, and Qiaozhu Mei. (2013). “One Theme in all Views: Modeling Consensus Topics in Multiple Contexts.” In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ISBN:978-1-4503-2174-7 doi:10.1145/2487575.2487682

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222013%22+One+Theme+in+all+Views%3A+Modeling+Consensus+Topics+in+Multiple+Contexts
• http://dl.acm.org/citation.cfm?id=2487575.2487682&preflayout=flat#citedby

Quotes

Author Keywords

• Co-regularization; information search and retrieval; multiple contexts; topic modeling; user-generated content

Abstract

New challenges have been presented to classical topic models when applied to social media, as user-generated content suffers from significant problems of data sparseness. A variety of heuristic adjustments to these models have been proposed, many of which are based on the use of context information to improve the performance of topic modeling. Existing contextualized topic models rely on arbitrary manipulation of the model structure, by incorporating various context variables into the generative process of classical topic models in an ad hoc manner. Such manipulations usually result in much more complicated model structures, sophisticated inference procedures, and low generalizability to accommodate arbitrary types or combinations of contexts. In this paper we explore a different direction. We propose a general solution that is able to exploit multiple types of contexts without arbitrary manipulation of the structure of classical topic models. We formulate different types of contexts as multiple views of the partition of the corpus. A co-regularization framework is proposed to let these views collaborate with each other, vote for the consensus topics, and distinguish them from view-specific topics. Experiments with real-world datasets prove that the proposed method is both effective and flexible to handle arbitrary types of contexts.

References

;

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2013 OneThemeinallViewsModelingConse	Qiaozhu Mei Jian Tang Ming Zhang			One Theme in all Views: Modeling Consensus Topics in Multiple Contexts				10.1145/2487575.2487682		2013