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 2.67% on the standard PTB test set, which represents 3.3% relative error reduction over the previous best result on the same data set, while using fewer features.
REF present the Guided Learning (GL) framework for bidirectional sequence classification.
Guided Learning for Bidirectional Sequence Classification
45th Annual Meeting of the Association of Computational Linguistics
Computer Science