Multiview Semi-supervised Learning for Ranking Multilingual Documents [chapter]

Nicolas Usunier, Massih-Reza Amini, Cyril Goutte
2011 Lecture Notes in Computer Science  
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 semisupervised 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
more » ... attention. We describe a semi-supervised multiview ranking algorithm that exploits a global agreement between viewspecific 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.
doi:10.1007/978-3-642-23808-6_29 fatcat:xdmqogqynje2tjfsdvzrfj4p7m