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. © 2011 Springer-Verlag.
CITATION STYLE
Usunier, N., Amini, M. R., & Goutte, C. (2011). Multiview semi-supervised learning for ranking multilingual documents. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6913 LNAI, pp. 443–458). https://doi.org/10.1007/978-3-642-23808-6_29
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