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Cross language information retrieval based on LDA

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3 Author(s)
Ai Wang ; Key Lab. of Complex Syst. & Intell. Sci., Chinese Acad. of Sci., Beijing, China ; YaoDong Li ; Wei Wang

This paper proposed a LDA-based cross-language retrieval model that did not rely on word-by-word translation of query or document. Instead, a parallel corpus was used to estimate a cross-language LDA (Latent Dirichlet Allocation) model. We assumed that a topic variable Z in LDA could generate both an English token and a Chinese token, given that the parallel corpus contained two languages: English and Chinese. Therefore, the LDA model was easy to be extended to multi-language information retrieval as long as a multi-lingual parallel corpus was provided. The proposed LDA-based crosslanguage retrieval model was compared with three popular retrieval models: LDA-based mono-lingual document model; Mono-lingual TF.IDF retrieval model; Cross-lingual Latent Semantic Indexing retrieval model on CNKI datasets. Experimental results showed that this model was very effective and achieved very good performance.

Published in:

Intelligent Computing and Intelligent Systems, 2009. ICIS 2009. IEEE International Conference on  (Volume:3 )

Date of Conference:

20-22 Nov. 2009