The paper introduces *Structural Correspondence Learning* (SCL) as a method to adapt discriminative models from a source domain to a target domain with limited or no labeled data. SCL aims to induce correspondences among features from different domains by modeling their correlations with *pivot* features, which are common and behave similarly in both domains. The key steps involve defining pivot features, creating binary classification problems for these features, and using structural learning techniques to model the correlations between pivot and non-pivot features. The authors test SCL on part-of-speech (PoS) tagging, demonstrating improved accuracy when adapting from the Wall Street Journal (WSJ) to MEDLINE data, even with limited labeled target domain data. They also show that SCL can enhance the performance of dependency parsers in the target domain. The paper discusses related work and concludes by highlighting the potential of SCL for various natural language processing tasks.The paper introduces *Structural Correspondence Learning* (SCL) as a method to adapt discriminative models from a source domain to a target domain with limited or no labeled data. SCL aims to induce correspondences among features from different domains by modeling their correlations with *pivot* features, which are common and behave similarly in both domains. The key steps involve defining pivot features, creating binary classification problems for these features, and using structural learning techniques to model the correlations between pivot and non-pivot features. The authors test SCL on part-of-speech (PoS) tagging, demonstrating improved accuracy when adapting from the Wall Street Journal (WSJ) to MEDLINE data, even with limited labeled target domain data. They also show that SCL can enhance the performance of dependency parsers in the target domain. The paper discusses related work and concludes by highlighting the potential of SCL for various natural language processing tasks.