Chunking with Max-Margin Markov Networks

被引：0
|
作者：
Tang Buzhou [1 ]
Wang Xuan [1 ]
Wang Xiaolong [1 ]
机构：
[1] Shenzhen Grad Sch, Harbin Inst Technol, Dept Comp Sci & Technol, Shenzhen 518055, Peoples R China
来源：
PACLIC 22: PROCEEDINGS OF THE 22ND PACIFIC ASIA CONFERENCE ON LANGUAGE, INFORMATION AND COMPUTATION | 2008年
关键词：
max-margin markov networks; graphical models; conditional random fields; support vector machines; generalization ability;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, we apply Max-Margin Markov Networks (M3Ns) to English base phrases chunking, which is a large margin approach combining both the advantages of graphical models(such as Conditional Random Fields, CRFs) and kernel-based approaches (such as Support Vector Machines, SVMs) to solve the problems of multi-label multi-class supervised classification. To show the efficiency of M3Ns, we compare it with CRFs and other relative systems on the data set of CoNLL-2000 comprehensively. The experiment results show that M3Ns achieves state-of-the-art performance with strong generalization ability, which is better than CRFs.
引用
收藏
页码：474 / 480
页数：7
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