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Optimization of hidden Markov model by a genetic algorithm for web information extraction

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成果类型:
期刊论文
作者:
Xiao, Jiyi*;Zou, Lamei;Li, Chuanqi
通讯作者:
Xiao, Jiyi
作者机构:
[Li, Chuanqi; Xiao, Jiyi; Zou, Lamei] Univ S China, Sch Comp Sci & Technol, Hengyang 421001, Peoples R China.
通讯机构:
[Xiao, Jiyi] U
Univ S China, Sch Comp Sci & Technol, Hengyang 421001, Peoples R China.
语种:
英文
关键词:
hidden Markov model;genetic algorithm;Baum-Welch algorithm;Viterbi algorithm;information extraction
期刊:
PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS AND KNOWLEDGE ENGINEERING (ISKE 2007)
ISSN:
1951-6851
年:
2007
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 demonstrates a new training method based on GA and Baum-Welch algorithms to obtain an HMM model with optimized number of states in the HMM models and its model parameters for web information extraction. This method is not only able to overcome the shortcomings of the slow convergence speed of the HMM approach. In addition, this method also finds better number of states in the HMM topology as well as its model parameters. From the experiments with the 2100 webs extracted from our corpus, this method is able to find the optimal topology in all cases. The experiments are found that the...

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