query model refinement using word graphs

	query model refinement using word graphs
	Huang Yunping; Sun Le; Nie Jian-Yun
	2010
会议名称	19th International Conference on Information and Knowledge Management and Co-located Workshops, CIKM'10
会议录名称	International Conference on Information and Knowledge Management, Proceedings
页码	1453-1456
会议日期	40842
会议地点	Toronto, ON, Canada
收录类别	EI
出版地	United States
ISBN	9781450000000
部门归属	(1) Institute of Software, Chinese Academy of Sciences, Beijing, China; (2) Department of Computer Science and Operations Research, University of Montreal, Canada
摘要	Pseudo relevance feedback method is an effective method for query model refinement. Most existing pseudo relevance feedback methods only take into consideration the term distribution of the feedback documents, but omit the term's context information. This paper presents a graph-based method to improve query models, in which a word graph is constructed to encode terms and their co-occurrence dependencies within the feedback documents. Using a random walk, the weight of each term in the graph can be determined in a context-dependent manner, i.e. the weight of a term is strongly dependent on the weights of the connected context terms. Our experimental results on four TREC collections show that our proposed approach is more effective than the existing state-of-the-art approaches. © 2010 ACM.
关键词	Image Retrieval Knowledge Management Query Processing Random Processes
主办者	ACM SIGIR; ACM SIGWEB; ACM SIGKDD
内容类型	会议论文
URI标识	http://ir.iscas.ac.cn/handle/311060/8874
专题	基础软件国家工程研究中心
推荐引用方式 GB/T 7714	Huang Yunping,Sun Le,Nie Jian-Yun. query model refinement using word graphs[C]. United States,2010:1453-1456.

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