PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A Co-Classification Approach to Learning from Multilingual Corpora
Massih Amini and Cyril Goutte
Machine Learning Journal Volume 79, Number 1-2, pp. 105-121, 2010.

Abstract

We address the problem of learning text categorization from a corpus of multilingual documents. We propose a multiview learning, co-regularization approach, in which we consider each language as a separate source, and minimize a joint loss that combines monolingual classification losses in each language while ensuring consistency of the categorization across languages. We derive training algorithms for logistic regression and boosting, and show that the resulting categorizers outperform models trained independently on each language, and even, most of the times, models trained on the joint bilingual data. Experiments are carried out on a multilingual extension of the RCV2 corpus, which is available for benchmarking.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Information Retrieval & Textual Information Access
ID Code:9289
Deposited By:Massih Amini
Deposited On:21 February 2012