A Hybrid Model for Ad-hoc Information Retrieval

被引:0
|
作者
Ye, Zheng [1 ]
Huang, Jimmy Xiangji [1 ]
Miao, Jun [2 ]
机构
[1] York Univ, Informat Retrieval & Knowledge Management Res Lab, Sch Informat Technol, Toronto, ON, Canada
[2] York Univ, Informat Retrieval & Knowledge Management Res Lab, Dept Comp Sci & Engn, Toronto, ON, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
Hybrid Model; Rocchio's Relevance Feedback;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Many information retrieval (IR) techniques have been proposed to improve the performance, and some combinations of these techniques has been demonstrated to be effective. However, how to effectively combine them is largely unexplored. It is possible that a method reduces the positive influence of the other one even if both of them are effective separately. In this paper, we propose a new hybrid model which can simply and flexibly combine components of three different IR techniques under a uniform framework. Extensive experiments on the TREC standard collections indicate that our proposed model can outperform the best TREC systems consistently in the ad-hoc retrieval. It shows that the combination strategy in our proposed model is very effective. Meanwhile, this method is also re-useable for other researchers to test whether their new methods are additive to the current technologies.
引用
收藏
页码:1025 / 1026
页数:2
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