We present a new approach to cross-language text classification that builds on structural correspondence learning, a recently proposed theory for domain adaptation .

Our approach builds upon structural correspondence learning, SCL, a recently proposed theory for domain adaptation in the field of natural language processing (Blitzer et al., 2006).

Domain Adaptation Domain adaptation refers to the problem of adapting a statistical classifier trained on data from one (or more) source domains (e.g., newswire texts) to a different target domain (e.g., legal texts).

In the basic domain adaptation setting we are given labeled data from the source domain and unlabeled data from the target domain, and the goal is to train a classifier for the target domain.

The latter setting is referred to as unsupervised domain adaptation .