摘要
针对网页非结构化信息抽取复杂度高的问题,提出了一种基于网页分割的Web信息提取算法。对网页噪音进行预处理,根据网页的文档对象模型树结构进行标签路径聚类,通过自动训练的阈值和网页分割算法快速判定网页的关键部分,根据数据块中的嵌套结构获取网页文本提取模板。对不同类型网站的实验结果表明,该算法运行速度快、准确度高。
This paper proposes a Web information extraction algorithm based on Web division to solve the high complexity problem of unstructured information extraction. The method adopts Web noise pretreatment, carries on the tag path clustering according to the document object model tree structure of Web. The key part of the Web is determined rapidly through automatic training threshold value and Web page segmentation algorithm, and Web text extracted templates are obtained according to nesting structure in the data block. Experimental results on different kinds of Web sites show that the algorithm is fast and accurate.
出处
《微型机与应用》
2011年第5期54-56,共3页
Microcomputer & Its Applications
基金
广东省软科学研究项目(2009B070300052)
关键词
网页分割
信息提取
聚类
阈值
Web page segmentation
information extraction
clustering
threshold
