LDA BASED PSEUDO RELEVANCE FEEDBACK FOR CROSS LANGUAGE INFORMATION RETRIEVAL

被引：0
|
作者：
Wang, Xuwen [1 ]
Zhang, Qiang [2 ]
Wang, Xiaojie [1 ]
Sun, Yueping [1 ]
机构：
[1] Beijing Univ Posts & Telecommun, Beijing 100876, Peoples R China
[2] State Grid Elect Power Res Inst, Beijing 100192, Peoples R China
来源：
2012 IEEE 2nd International Conference on Cloud Computing and Intelligent Systems (CCIS) Vols 1-3 | 2012年
关键词：
Pseudo relevance feed back; Latent Dirichlet Allocation (LDA); Query expansion; Vector Space Model (VSM); Cross language information retrieval;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper introduced a LDA-based pseudo relevance feedback (PRF) model for cross language information retrieval. To validate the performance of PRF techniques in CLIR task, we conducted cross language query expansion experiments based on a self-constructed CLIR system, the LDA-based PRF model was applied before or after the query translating process, namely the pre-translation-PRF, the post-translation-PRF, and the combined-PRF strategy. We also compared this model with the classical VSM-based-PRF algorithm. Experiment results showed that the proposed LDA-based PRF method was effective for improving the performance of CLIR.
引用
收藏
页码：1511 / 1516
页数：6
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