| Download | - View accepted manuscript: Combining Coregularization and Consensus-Based Self-Training for Multilingual Categorization (PDF, 654 KiB)
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| DOI | Resolve DOI: https://doi.org/10.1145/1835449.1835529 |
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| Author | Search for: Amini, Massih-Reza1; Search for: Goutte, Cyril1; Search for: Usunier, Nicolas |
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| Affiliation | - National Research Council Canada. NRC Institute for Information Technology
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| Format | Text, Article |
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| Conference | 33rd Annual Association for Computing Machinery’s Special Interest Group on Information Retrieval Conference (ACM SIGIR), July 19-23, 2010, Geneva, Switzerland |
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| Subject | Multilingual Document Classification; Learning from Multiple Views; Semi-supervised Learning |
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| Abstract | We investigate the problem of learning document classifiers in a multilingual setting, from collections where labels are only partially available. We address this problem in the framework of multiview learning, where different languages correspond to different views of the same document, combined with semi-supervised learning in order to benefit from unlabeled documents. We rely on two techniques, coregularization and consensus-based self-training, that combine multiview and semi-supervised learning in different ways. Our approach trains different monolingual classifiers on each of the views, such that the classifiers’ decisions over a set of unlabeled examples are in agreement as much as possible, and iteratively labels new examples from another unlabeled training set based on a consensus across language-specific classifiers. We derive a boosting-based training algorithm for this task, and analyze the impact of the number of views on the semi-supervised learning results on a multilingual extension of the Reuters RCV1/RCV2 corpus using five different languages. Our experiments show that coregularization and consensus-based self-training are complementary and that their combination is especially effective in the interesting and very common situation where there are few views (languages) and few labeled documents available. |
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| Publication date | 2010-07-19 |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| NPARC number | 15469835 |
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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 | cf783c37-e5ce-4280-a7cc-9e0b865245e7 |
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| Record created | 2010-06-10 |
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| Record modified | 2020-04-17 |
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