2012 LatentAssociationAnalysisofDocu

• (Miao et al., 2012) ⇒ Gengxin Miao, Ziyu Guan, Louise E. Moser, Xifeng Yan, Shu Tao, Nikos Anerousis, and Jimeng Sun. (2012). “Latent Association Analysis of Document Pairs.” In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2012). ISBN:978-1-4503-1462-6 doi:10.1145/2339530.2339752

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222012%22+Latent+Association+Analysis+of+Document+Pairs
• http://dl.acm.org/citation.cfm?id=2339530.2339752&preflayout=flat#citedby

Quotes

Author Keywords

• General; ranking algorithm; topic model; variational inference

Abstract

This paper presents Latent Association Analysis (LAA), a generative model that analyzes the topics within two document sets simultaneously, as well as the correlations between the two topic structures, by considering the semantic associations among document pairs. LAA defines a correlation factor that represents the connection between two documents, and considers the topic proportion of paired documents based on this factor. Words in the documents are assumed to be randomly generated by particular topic assignments and topic-to-word probability distributions. The paper also presents a new ranking algorithm, based on LAA, that can be used to retrieve target documents that are potentially associated with a given source document. The ranking algorithm uses the latent factor in LAA to rank target documents by the strength of their semantic associations with the source document. We evaluate the LAA algorithm with real datasets, specifically, the IT-Change and the IT-Solution document sets from the [[IBM IT service environment]] and the Symptom-Treatment document sets from Google Health. Experimental results demonstrate that the LAA algorithm significantly outperforms existing algorithms.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2012 LatentAssociationAnalysisofDocu	Xifeng Yan Ziyu Guan Jimeng Sun Gengxin Miao Louise E. Moser Shu Tao Nikos Anerousis			Latent Association Analysis of Document Pairs				10.1145/2339530.2339752		2012