Abstract
We study in this paper the combination of different concept detection methods for conceptual indexing. Conceptual indexing shows effective results when large knowledge bases are available. But concept detection is not always accurate and errors limit interest of concept usage. A solution to solve this problem is to combine different concept detection methods. In this paper, we investigate several ways to combine concept detection methods, both on queries and documents, within the framework of the language modeling approach to IR. Our experiments show that our model fusion improves the standard language model by up to 17% on mean average precision.
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Maisonnasse, L., Gaussier, E., Chevallet, JP. (2009). Model Fusion in Conceptual Language Modeling. In: Boughanem, M., Berrut, C., Mothe, J., Soule-Dupuy, C. (eds) Advances in Information Retrieval. ECIR 2009. Lecture Notes in Computer Science, vol 5478. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00958-7_23
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DOI: https://doi.org/10.1007/978-3-642-00958-7_23
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