Semi-supervised maximum entropy based POS tagging for large scale Chinese corpus

被引：0

作者：

Yuan, Caixia ^{[1
]}

Wang, Xiaojie ^{[1
]}

Zhai, Junjie ^{[1
]}

机构：

[1] Beijing Univ Posts & Telecommun, Sch Informat Engn, Beijing 100876, Peoples R China

来源：

PROCEEDINGS OF 2006 INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE: 50 YEARS' ACHIEVEMENTS, FUTURE DIRECTIONS AND SOCIAL IMPACTS | 2006年

关键词：

semi-supervised; maximum entropy; Chinese POS tagging;

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 issues related to POS tagging for large-scale Chinese corpus using the maximum entropy technique, in which unlabeled data are introduced for compensating the sparseness and inconsistency of labeled data. We test our method on the corpus of Peking University China and show that as much as 27% error reduction is obtained by semi-supervised strategy.

引用

页码：385 / 389

页数：5

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