Download | - View accepted manuscript: Multiview semi-supervised learning for ranking multilingual documents (PDF, 357 KiB)
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DOI | Resolve DOI: https://doi.org/10.1007/978-3-642-23808-6_29 |
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Author | Search for: Usunier, Nicolas; Search for: Amini, Massih-Reza1; Search for: Goutte, Cyril1 |
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Affiliation | - National Research Council of Canada
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Format | Text, Book Chapter |
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Conference | European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2011). Athens, Greece. September 5-9, 2011 |
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Subject | learning to rank; semi-supervised learning; multiview learning |
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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 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. |
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Publication date | 2011 |
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Publisher | Springer Berlin Heidelberg |
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Series | |
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Language | English |
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Peer reviewed | No |
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NPARC number | 18608906 |
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Export citation | Export as RIS |
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Report a correction | Report a correction (opens in a new tab) |
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Record identifier | cbacfa57-40f8-42ad-bf51-5e1417b443bf |
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Record created | 2011-09-28 |
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Record modified | 2020-03-03 |
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