基于广义隐马尔可夫模型的网页信息抽取方法  

Web information extraction based on a generalized hidden Markov model

作　　者：王静[1] 姚勇[1] 刘志镜[1] 

机构地区：[1]西安电子科技大学计算机学院,陕西西安710071

出　　处：《山东大学学报（理学版）》2007年第11期49-52,共4页Journal of Shandong University(Natural Science)

基　　金：国家自然科学基金资助项目(60573139)

摘　　要：针对网页所特有的基于版面结构的特点,利用基于视觉的网页分割算法VIPS对网页分块,得到一种新的状态转移序列,取代了传统的状态转移序列。通过二阶Markov链改进广义隐马尔可夫模型(GHMM)的状态转移和输出观测值假设条件,提出了二阶的广义隐马尔可夫模型。最后通过实验说明改进的GHMM对于网页信息抽取有很高的精确率。Since web pages are based on the web-specific layout structure feature, instead of using the transitional sequential state transition order, a new state transition order was proposed by using a vision based page segmentation algorithm （VIPS）. In addit- ion, the supposed state transition and the emission symbol conditions were improved by using the second-order Markov chain, and then a novel generalized hidden Markov model （GHMM） was proposed based on the improvement. Finally,through an example, it shows that the modified GHMM has a very high precision for web information extraction.

关 键 词：基于视觉的网页分割 广义隐马尔可夫模型 二阶Markov链 WEB信息抽取 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]