Multiview semi-supervised learning for ranking multilingual documents

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TypeBook Chapter
Proceedings titleMachine Learning and Knowledge Discovery in Databases : European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011, Proceedings, Part III
Series titleLecture Notes In Computer Science; Volume 6913
ConferenceEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2011). Athens, Greece. September 5-9, 2011
Pages443458; # of pages: 16
SubjectLearning to rank; Semi-supervised learning; Multiview learning
AbstractWe 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 less 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.
Publication date
PublisherSpringer Berlin Heidelberg
AffiliationNational Research Council Canada
Peer reviewedNo
NPARC number18608906
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Record identifiercbacfa57-40f8-42ad-bf51-5e1417b443bf
Record created2011-09-28
Record modified2016-07-15
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