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• W142374489 abstract "In this paper we study cross-language information retrieval using a bilingual topic model trained on comparable corpora such as Wikipedia articles. The bilingual Latent Dirichlet Allocation model (BiLDA) creates an interlingual representation, which can be used as a translation resource in many different multilingual settings as comparable corpora are available for many language pairs. The probabilistic interlingual representation is incorporated in a statistical language model for information retrieval. Experiments performed on the English and Dutch test datasets of the CLEF 2001-2003 CLIR campaigns show the competitive performance of our approach compared to cross-language retrieval methods that rely on pre-existing translation dictionaries that are hand-built or constructed based on parallel corpora." @default.
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• W142374489 date "2011-01-01" @default.
• W142374489 modified "2023-09-27" @default.
• W142374489 title "Cross-Language Information Retrieval with Latent Topic Models Trained on a Comparable Corpus" @default.
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• W142374489 doi "https://doi.org/10.1007/978-3-642-25631-8_4" @default.
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