PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Multiview Semi-Supervised Learning for Ranking Multilingual Documents
Nicolas Usunier, Massih Amini and Cyril Goutte
In: ECML-PKDD 2011, 5-9 Sep 2011, Greece.

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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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:9301
Deposited By:Massih Amini
Deposited On:22 February 2012