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A hybrid approach for web information extraction

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成果类型:
期刊论文、会议论文
作者:
Xiao, Ji-Yi*;Zhu, Dao-Hui;Zou, La-Mei
通讯作者:
Xiao, Ji-Yi
作者机构:
[Zou, La-Mei; Zhu, Dao-Hui; Xiao, Ji-Yi] Univ S China, Sch Comp Sci & Technol, Hengyang 421001, Peoples R China.
通讯机构:
[Xiao, Ji-Yi] U
Univ S China, Sch Comp Sci & Technol, Hengyang 421001, Peoples R China.
语种:
英文
关键词:
Information eztraction;Hidden Markov model;Mazimum entropy;Mazimum entropy Markov model;Generalized iterative scaling
期刊:
Proceedings of the 7th International Conference on Machine Learning and Cybernetics, ICMLC
年:
2008
卷:
3
页码:
1560-1563
会议名称:
2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)
会议论文集名称:
2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)论文集
会议时间:
2008-07-12
会议地点:
昆明
会议赞助商:
河北大学
机构署名:
本校为第一且通讯机构
院系归属:
计算机科学与技术学院
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 t...

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