Combining Coregularization and Consensus-Based Self-Training for Multilingual Text Categorization

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.
Document type :
Conference papers
Complete list of metadatas

https://hal.archives-ouvertes.fr/hal-01291883
Contributor : Lip6 Publications <>
Submitted on : Tuesday, March 22, 2016 - 11:38:07 AM
Last modification on : Thursday, March 21, 2019 - 1:04:52 PM

Identifiers

Citation

Massih-Reza Amini, Cyril Goutte, Nicolas Usunier. Combining Coregularization and Consensus-Based Self-Training for Multilingual Text Categorization. The 33rd Annual ACM SIGIR Conference (SIGIR 2010), Jul 2010, Geneva, Switzerland. pp.475-482, ⟨10.1145/1835449.1835529⟩. ⟨hal-01291883⟩

Share

Metrics

Record views

82