Multiview Semi-Supervised Learning for Ranking Multilingual Documents

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 applicatin to the problem of ranking has received much less attention. We describe a semi-supervised multi-veiw 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 suugest that our approach is most effective when few labeled documents are available and the classes are imbalanced.
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Communication dans un congrès
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Sep 2011, Athens, Greece. Springer, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 6913, pp.443-458, Lecture Notes in Computer Science. 〈10.1007/978-3-642-23808-6_29〉
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https://hal.archives-ouvertes.fr/hal-01286156
Contributeur : Lip6 Publications <>
Soumis le : jeudi 10 mars 2016 - 13:33:00
Dernière modification le : jeudi 22 novembre 2018 - 14:22:32

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Nicolas Usunier, Massih-Reza Amini, Cyril Goutte. Multiview Semi-Supervised Learning for Ranking Multilingual Documents. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Sep 2011, Athens, Greece. Springer, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 6913, pp.443-458, Lecture Notes in Computer Science. 〈10.1007/978-3-642-23808-6_29〉. 〈hal-01286156〉

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