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.
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Vulić, I., De Smet, W., Moens, MF. (2011). Cross-Language Information Retrieval with Latent Topic Models Trained on a Comparable Corpus. In: Salem, M.V.M., Shaalan, K., Oroumchian, F., Shakery, A., Khelalfa, H. (eds) Information Retrieval Technology. AIRS 2011. Lecture Notes in Computer Science, vol 7097. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25631-8_4
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DOI: https://doi.org/10.1007/978-3-642-25631-8_4
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