Cross-Language Text Classification Using Structural Correspondence Learning

Abstract

We present a new approach to cross-language text classification that builds on structural correspondence learning, a re-cently proposed theory for domain adap-tation. The approach uses unlabeled doc-uments, along with a simple word trans-lation oracle, in order to induce task-specific, cross-lingual word correspon-dences. We report on analyses that reveal quantitative insights about the use of un-labeled data and the complexity of inter-language correspondence modeling. We conduct experiments in the field of cross-language sentiment classification, employing English as source language, and German, French, and Japanese as tar-get languages. The results are convincing; they demonstrate both the robustness and the competitiveness of the presented ideas.