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• W61058509 abstract "We address the problem of learning to rank documents in a multilingual context, when reference ranking information is only partially available. We propose a multiview learning approach to this semi-supervised ranking task, where the translation of a document in a given language is considered as a view of the document. Although both multiview and semi-supervised learning of classifiers have been studied extensively in recent years, their application to the problem of ranking has received much less attention. We describe a semi-supervised multiview ranking algorithm that exploits a global agreement between view-specific ranking functions on a set of unlabeled observations. We show that our proposed algorithm achieves significant improvements over both semi-supervised multiview classification and semi-supervised single-view rankers on a large multilingual collection of Reuters news covering 5 languages. Our experiments also suggest that our approach is most effective when few labeled documents are available and the classes are imbalanced." @default.
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• W61058509 date "2011-01-01" @default.
• W61058509 modified "2023-09-23" @default.
• W61058509 title "Multiview Semi-supervised Learning for Ranking Multilingual Documents" @default.
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• W61058509 doi "https://doi.org/10.1007/978-3-642-23808-6_29" @default.
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