Discriminative learning framework is one of the very successful fields of machine learning. The methods of this paradigm, such as Boosting, and Support Vector Machines have significantly advanced the state-of-the-art for classification by improving the accuracy and by increasing the applicability of machine learning methods. One of the key benefits of these methods is their ability to learn efficiently in high dimensional feature spaces, either by the use of implicit data representations via kernels or by explicit feature induction. However, traditionally these methods do not exploit dependencies between class labels where more than one label is predicted. Many real-world classification problems involve sequential, temporal or structural dependencies between multiple labels. We will investigate recent research on generalizing discriminative methods to learning in structured domains. These techniques combine the efficiency of dynamic programming methods with the advantages of the state-of-the-art learning methods.