We present a new approach to crosslanguage text classification that builds on structural correspondence learning, a recently proposed theory for domain adaptation. The approach uses unlabeled documents, along with a simple word translation oracle, in order to induce taskspecific, cross-lingual word correspondences. We report on analyses that reveal quantitative insights about the use of unlabeled data and the complexity of interlanguage correspondence modeling. We conduct experiments in the field of cross-language sentiment classification, employing English as source language, and German, French, and Japanese as target languages. The results are convincing; they demonstrate both the robustness and the competitiveness of the presented ideas.
REF provided a CL-SCL model based on structural correspondence learning (SCL) for sentiment classification.
Cross-Language Text Classification Using Structural Correspondence Learning
ACL
Computer Science