Discriminative learning techniques for sequential data have proven to be more effective than generative models for named entity recognition, information extraction, and other tasks of discrimination. However, semi-supervised learning mechanisms that utilize inexpensive unlabeled sequences in addition to few labeled sequences – such as the Baum-Welch algorithm – are available only for generative models. The multi-view approach is based on the principle of maximizing the consensus among multiple independent hypotheses; we develop this principle into a semisupervised hidden Markov perceptron algorithm. Experiments reveal that the resulting procedure utilizes unlabeled data effectively and discriminates more accurately than its purely supervised counterparts.