Multiview Semi-supervised Learning for Ranking Multilingual Documents

  • Nicolas Usunier
  • Massih-Reza Amini
  • Cyril Goutte
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6913)


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.


Learning to Rank Semi-supervised Learning Multiview Learning 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Nicolas Usunier
    • 1
  • Massih-Reza Amini
    • 2
  • Cyril Goutte
    • 2
  1. 1.LIP6Université Pierre et Marie CurieParis 5 cedexFrance
  2. 2.National Research Council CanadaIITGatineauCanada

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