基于模拟退火算法和二阶HMM的Web信息抽取

Web information extraction based on simulated annealing algorithm and second-order HMM

针对传统隐马尔科夫模型对初值敏感和未考虑历史状态的问题,提出了使用模拟退火算法训练二阶隐马尔科夫模型参数的SA-HMM2。在基于SA-HMM2的Web信息抽取方法中,采用基于视觉的网页分割算法VIPS对网页分块得到状态转移序列,利用提出的SA-HMM2训练算法获取HMM2全局最优模型参数,用改进的Viterbi算法实现了Web信息的抽取。实验结果表明,该方法在平均综合值方面比HMM、GA-HMM分别提高约21%和7%。

The traditional hidden markov model （HMM） is sensitive to the initial model parameters and does not consider the is- sue of historical state. The SA-HMM2 algorithm is proposed, which utilizes the simulated annealing （SA） algorithm to train the parameters of the secon＆order HMM （HMM2）. In the Web information extraction method based on SA-HMM2, a state transi- tion sequence is obtained by using the vision-based page segmentation algorithm （V1PS）. The proposed SA-HMM2 training algo- rithm is used for calculating the global optimal parameters of HMM2. The improved Viterbi algorithm is implemented for Web information extraction. Compared with HMM and GA-HMM, experimental results show that the new strategy increases the ex- traction performance by 21% and 7% respectively.