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In many multilingual text classification problems, the documents in differentlanguages often share the same set of categories. To reduce the labeling costof training a classification model for each individual language, it isimportant to transfer the label knowledge gained from one language to anotherlanguage by conducting cross language classification. In this paper we developa novel subspace co-regularized multi-view learning method for cross languagetext classification. This method is built on parallel corpora produced bymachine translation. It jointly minimizes the training error of each classifierin each language while penalizing the distance between the subspacerepresentations of parallel documents. Our empirical study on a large set ofcross language text classification tasks shows the proposed method consistentlyoutperforms a number of inductive methods, domain adaptation methods, andmulti-view learning methods.

Cross Language Text Classification via Subspace Co-Regularized Multi-View Learning