query model refinement using word graphs
文献类型：会议论文
| 作者 | Huang Yunping ; Sun Le ; Nie Jian-Yun |
| 出版日期 | 2010 |
| 会议名称 | 19th International Conference on Information and Knowledge Management and Co-located Workshops, CIKM'10 |
| 会议日期 | 40842 |
| 会议地点 | Toronto, ON, Canada |
| 关键词 | Image retrieval Knowledge management Query processing Random processes |
| 页码 | 1453-1456 |
| 英文摘要 | 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. |
| 收录类别 | EI |
| 会议主办者 | ACM SIGIR; ACM SIGWEB; ACM SIGKDD |
| 会议录 | International Conference on Information and Knowledge Management, Proceedings
 |
| 会议录出版地 | United States |
| ISBN号 | 9781450000000 |
| 源URL | [http://124.16.136.157/handle/311060/8874]  |
| 专题 | 软件研究所_基础软件国家工程研究中心_会议论文 |
| 推荐引用方式 GB/T 7714 | 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. |
入库方式： OAI收割
来源：软件研究所
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