Revision Learning and its Application to Part-of-Speech Tagging

Tetsuji Nakagawa, Taku Kudo, Yuji Matsumoto

Abstract: This paper presents a revision learning method that achieves high performance with small computational cost by combining a model with high generalization capacity and a model with small computational cost. This method uses a high capacity model to revise the output of a small cost model. We apply this method to English part-of-speech tagging and Japanese morphological analysis, and show that the method performs well.