Abstract
This paper considers centralized and distributed architectures for multilingual information retrieval. Several merging strategies, including raw-score merging, round-robin merging, normalized-score merging, and normalized-by-top-k merging, were investigated. The effects of translation penalty on merging was also examined. The experimental results show that the centralized approach is better than the distributed approach. In the distributed approach, the normalized-by-top-k merging with translation penalty outperforms other merging strategies, except for raw-score merging. Because the performances of English to other languages are similar, raw-score merging gives better performance in our experiments. However, raw-score merging is not workable in practice if different IR systems are adopted.
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Lin, WC., Chen, HH. (2003). Merging Mechanisms in Multilingual Information Retrieval. In: Peters, C., Braschler, M., Gonzalo, J., Kluck, M. (eds) Advances in Cross-Language Information Retrieval. CLEF 2002. Lecture Notes in Computer Science, vol 2785. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45237-9_14
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DOI: https://doi.org/10.1007/978-3-540-45237-9_14
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