In this paper, we propose guided learning, a new learning framework for bidirectional sequence classification. The tasks of learning the order of inference and training the local classifier are dynamically incorporated into a single Perceptron like learning algorithm. We apply this novel learning algorithm to POS tagging. It obtains an error rate of on the standard PTB test set, which represents relative error reduction over the previous best result on the same data set, while using fewer features. .
