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Title:
query model refinement using word graphs
Author: Huang Yunping ; Sun Le ; Nie Jian-Yun
Source: International Conference on Information and Knowledge Management, Proceedings
Conference Name: 19th International Conference on Information and Knowledge Management and Co-located Workshops, CIKM'10
Conference Date: 40842
Issued Date: 2010
Conference Place: Toronto, ON, Canada
Keyword: Image retrieval ; Knowledge management ; Query processing ; Random processes
Publish Place: United States
Indexed Type: EI
ISBN: 9781450000000
Department: (1) Institute of Software, Chinese Academy of Sciences, Beijing, China; (2) Department of Computer Science and Operations Research, University of Montreal, Canada
Sponsorship: ACM SIGIR; ACM SIGWEB; ACM SIGKDD
English Abstract: 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.
Content Type: 会议论文
URI: http://ir.iscas.ac.cn/handle/311060/8874
Appears in Collections:基础软件国家工程研究中心_会议论文

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Recommended Citation:
Huang Yunping,Sun Le,Nie Jian-Yun. query model refinement using word graphs[C]. 见:19th International Conference on Information and Knowledge Management and Co-located Workshops, CIKM'10. Toronto, ON, Canada. 40842.
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