Guided Learning for Bidirectional Sequence Classification
Libin Shen, Giorgio Satta, Aravind K. Joshi · 2007
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. 1