Multiview Semi-supervised Learning for Ranking Multilingual Documents

Nicolas Usunier (2), Massih-Reza Amini(1), Cyril Goutte (1)
(1) National Research Council Canada             (2) Laboratoire d'Informatique Paris 6
       123, boulevard Alexandre Taché                                 4, Place de Jussieu              
            Gatineau, Canada                                           75252 Paris 5 cedex         

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.