Cross - lingual Information Retrieval Model based on Bilingual Topic Correlation ⋆
نویسندگان
چکیده
How to construct relationship between bilingual texts is important to effectively processing multi-lingual text data and cross language barriers. Cross-lingual latent semantic indexing (CL-LSI) corpus-based doesnot fully take into account bilingual semantic relationship. The paper proposes a new model building semantic relationship of bilingual parallel document via partial least squares (PLS). In the model, the parallel documents are viewed as two different lingual representations for the same semantic object and a single latent semantic space for each language is created. The task of cross-lingual information retrieval (CLIR) is performed in the new bilingual latent semantic spaces. Experimental results on the ChineseEnglish aligned news stories collected from bilingual news website of the Wall Street Journal and Finance Times show that performance of CLIR in bilingual semantic spaces built by the presented model is over CL-LSI.
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