Revision Learning and its Application to Part-of-Speech Tagging
نویسندگان
چکیده
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 partof-speech tagging and Japanese morphological analysis, and show that the method performs well.
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تاریخ انتشار 2002