Paper: Cross-Language Text Classification Using Structural Correspondence Learning

ACL ID P10-1114
Title Cross-Language Text Classification Using Structural Correspondence Learning
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2010
Authors

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.