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query model refinement using word graphs
Huang Yunping; Sun Le; Nie Jian-Yun
2010
Conference Name19th International Conference on Information and Knowledge Management and Co-located Workshops, CIKM'10
SourceInternational Conference on Information and Knowledge Management, Proceedings
Pages1453-1456
Conference Date40842
Conference PlaceToronto, ON, Canada
Indexed TypeEI
Publish PlaceUnited States
ISBN9781450000000
Department(1) Institute of Software, Chinese Academy of Sciences, Beijing, China; (2) Department of Computer Science and Operations Research, University of Montreal, Canada
English AbstractPseudo 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.
KeywordImage Retrieval Knowledge Management Query Processing Random Processes
SponsorshipACM SIGIR; ACM SIGWEB; ACM SIGKDD
Content Type会议论文
URIhttp://ir.iscas.ac.cn/handle/311060/8874
Collection基础软件国家工程研究中心
Recommended Citation
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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