Markov graphic method for information retrieval

Authors:
Jiali Zuo;Mingwen Wang;Hao Ye
Affiliations:
School of information technology, Jiangxi university of Finance and Economics, Nanchang, China and School of advanced Vocation and Technology, Jiangxi Normal University, Nanchang, China;School of Computer and Information Engineering, Jiangxi Normal University, Nanchang, China;Math and Computer Science Department, Jiangxi Science and Technolocy, Normal University, Nanchang, China
Venue:
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part II
Year:
2011

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Abstract

Information retrieval model is central in information retrieval, which have been studied by many researchers. But over the decade, no single retrieval model has proven to be most effective. One of the reasons is the term independent assumption. Research have shown that adding useful information to retrieval model can improve the performance of retrieval model. As graphical model can model information effectively, we use Markov network to construct the term relationship, and model the term relationship and information retrieval model in a unified framework. Experimental results show that our model can improve the retrieval performance.