Supervised and Semi-supervised Machine Learning Ranking

Jean-Noël Vittaut 1 Patrick Gallinari 1
1 MALIRE - Machine Learning and Information Retrieval
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : We present a Semi-supervised Machine Learning based ranking model which can automatically learn its parameters using a training set of a few labeled and unlabeled 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 supervised and semi-supervised algorithms on CO-Focussed and CO-Thourough tasks using a baseline model which is an adaptation of Okapi to Structured Information Retrieval.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-01352074
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Submitted on : Friday, August 5, 2016 - 2:17:41 PM
Last modification on : Thursday, March 21, 2019 - 1:10:10 PM

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Jean-Noël Vittaut, Patrick Gallinari. Supervised and Semi-supervised Machine Learning Ranking. Advances in XML Information Retrieval and Evaluation: Fifth Workshop of the INitiative for the Evaluation of XML Retrieval (INEX'06), Dec 2006, Dagstuhl, Germany. pp.213-222, ⟨10.1007/978-3-540-73888-6_21⟩. ⟨hal-01352074⟩

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