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Cross Language Text Classification via Multi-view Subspace Learning

Cross Language Text Classification via Multi-view Subspace Learning

This video was recorded at NIPS Workshops, Lake Tahoe 2012. Cross language classification is an important task in multilingual learning, aiming for reducing the labeling cost of training a different classification model for each individual language. In this paper we develop a novel subspace co-regularized multi-view learning method for cross language text classification. The empirical study on a set of cross language text classification tasks shows the proposed method consistently outperforms a number of inductive methods, domain adaptation methods, and multi-view learning methods.
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