LDA BASED PSEUDO RELEVANCE FEEDBACK FOR CROSS LANGUAGE INFORMATION RETRIEVAL
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.
Pseudo relevance feed back Latent Dirichlet Allocation (LDA) Query expansion Vector Space Model (VSM) Cross language information retrieval
Xuwen Wang Qiang Zhang Xiaojie Wang Yueping Sun
Beijing University of Posts and Telecommunications,Beijing 100876,China State Grid Electric Power Research Institute,Beijing 100192,China
国际会议
杭州
英文
1993-1998
2012-10-30(万方平台首次上网日期,不代表论文的发表时间)