Over the last five years, we have been able to extend the theory of linear classifiers to structure prediction problems, combining the benefits of discriminative learning and of structured probabilistic models like hidden Markov models. I will review these models and their learning algorithms, and exemplify their use in text processing, with a focus on information extraction from biomedical text.