A hybrid approach for web information extraction

被引：1
|
作者：
Xiao, Ji-Yi [1 ]
Zhu, Dao-Hui [1 ]
Zou, La-Mei [1 ]
机构：
[1] Univ S China, Sch Comp Sci & Technol, Hengyang 421001, Peoples R China
来源：
PROCEEDINGS OF 2008 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7 | 2008年
关键词：
information extraction; hidden Markov model; maximum entropy; maximum entropy Markov model; generalized iterative scaling;
D O I：
10.1109/ICMLC.2008.4620654
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper presents a new approach based on maximum entropy and maximum entropy Markov model for web information extraction. This approach is not only able to overcome the shortcoming of the less precision and recall of the hidden Markov model. In addition, this approach can make the most of various kinds of contextual information from web. The experiments are found that the hybrid approach has an average precision rate of 87.516% while the hidden Markov model trained by the Baum-Welch algorithm has an average precision rate of 68.630%. This implies that the hybrid approach is more optimized than the hidden Markov model trained by the Baum-Welch algorithm.
引用
收藏
页码：1560 / 1563
页数：4
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