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1
Combining NLP and probabilistic categorisation fordocument and term selection for Swiss-Prot medical annotation
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2
Multiview Semi-Supervised Learning for Ranking Multilingual Documents
In: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases ; https://hal.archives-ouvertes.fr/hal-01286156 ; European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Sep 2011, Athens, Greece. pp.443-458, ⟨10.1007/978-3-642-23808-6_29⟩ (2011)
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3
A Co-classification Approach to Learning from Multilingual Corpora
In: ISSN: 0885-6125 ; EISSN: 1573-0565 ; Machine Learning ; https://hal.archives-ouvertes.fr/hal-01172633 ; Machine Learning, Springer Verlag, 2010, 79 (1-2), pp.105-121. ⟨10.1007/s10994-009-5151-5⟩ (2010)
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4
Multiview Clustering of Multilingual Documents
In: Proceedings of the 33rd Annual ACM SIGIR Conference (SIGIR 2010) ; The 33rd Annual ACM SIGIR Conference (SIGIR 2010) ; https://hal.archives-ouvertes.fr/hal-01292100 ; The 33rd Annual ACM SIGIR Conference (SIGIR 2010), Jul 2010, Geneva, Switzerland. pp.812-822, ⟨10.1145/1835449.1835633⟩ (2010)
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5
Combining Coregularization and Consensus-Based Self-Training for Multilingual Text Categorization
In: Proceedings of the 33rd Annual ACM SIGIR Conference (SIGIR 2010) ; The 33rd Annual ACM SIGIR Conference (SIGIR 2010) ; https://hal.archives-ouvertes.fr/hal-01291883 ; The 33rd Annual ACM SIGIR Conference (SIGIR 2010), Jul 2010, Geneva, Switzerland. pp.475-482, ⟨10.1145/1835449.1835529⟩ (2010)
Abstract: International audience ; 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.
Keyword: [INFO]Computer Science [cs]
URL: https://doi.org/10.1145/1835449.1835529
https://hal.archives-ouvertes.fr/hal-01291883
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6
Learning machine translation
Goutte, Cyril. - Cambridge, Mass. [u.a.] : MIT Press, 2009
BLLDB
UB Frankfurt Linguistik
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7
Learning from Multiple Partially Observed Views -- an Application to Multilingual Text Categorization
In: Advances in Neural Information Processing Systems ; https://hal.archives-ouvertes.fr/hal-01297947 ; Advances in Neural Information Processing Systems, Dec 2009, Vancouver, Canada (2009)
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8
Fast & Confident Probabilistic Categorization
Goutte, Cyril. - 2007
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9
Statistical Phrase-based Post-editing
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10
Combining NLP and probabilistic categorisation for document and term selection for Swiss-Prot medical annotation
Dobrokhotov, Pavel B.; Goutte, Cyril; Veuthey, Anne-Lise. - : Oxford University Press, 2003
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