In cross-lingual text classification problems, it is costly and time-consuming to annotate documents for each individual language. To avoid the expensive re-labeling process, domain adaptation techniques can be applied to adapt a learning system trained in one language domain to another language domain. In this paper we develop a transductive subspace representation learning method to address domain adaptation for cross-lingual text classifications. The proposed approach is formulated as a nonnegative matrix factorization problem and solved using an iterative optimization procedure. Our empirical study on cross-lingual text classification tasks shows the proposed approach consistently outperforms a number of comparison methods.

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12th IEEE International Conference on Data Mining, ICDM 2012
School of Computer Science

Guo, Y, & Xiao, M. (Min). (2012). Transductive representation learning for cross-lingual text classification. In Proceedings - IEEE International Conference on Data Mining, ICDM (pp. 888–893). doi:10.1109/ICDM.2012.29