Abstract
We present a Machine Learning based ranking model which can automatically learn its parameters using a training set of annotated examples composed of queries and relevance judgments on a subset of the document elements. Our model improves the performance of a baseline Information Retrieval system by optimizing a ranking loss criterion and combining scores computed from doxels and from their local structural context. We analyze the performance of our algorithm on CO-Focussed and CO-Thourough tasks and compare it to the baseline model which is an adaptation of Okapi to Structured Information Retrieval.
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Vittaut, JN., Gallinari, P. (2006). Machine Learning Ranking and INEX’05. In: Fuhr, N., Lalmas, M., Malik, S., Kazai, G. (eds) Advances in XML Information Retrieval and Evaluation. INEX 2005. Lecture Notes in Computer Science, vol 3977. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-34963-1_25
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DOI: https://doi.org/10.1007/978-3-540-34963-1_25
Publisher Name: Springer, Berlin, Heidelberg
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