Revision learning and its application to part-of-speech tagging

被引：0

作者：

Nakagawa, T ^{[1
]}

Kudo, T ^{[1
]}

Matsumoto, Y ^{[1
]}

机构：

[1] Nara Inst Sci & Technol, Grad Sch Informat Sci, Nara 6300101, Japan

来源：

40TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, PROCEEDINGS OF THE CONFERENCE | 2002年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：497 / 504

页数：8

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