基于相似页面的网络新闻内容自动提取

Chunyuan Zhang, Z. Lin
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引用次数: 4

摘要

今天,大多数新闻页面都是从一些底层结构化源生成的,因此我们认为依赖于模板的包装器应该比独立于模板的包装器更适合它们。本文提出了一种基于相似页面的基于模板的Web新闻内容自动提取方法。首先,我们选择两个相似的页面作为训练样本,并将它们表示为两个HTML DOM树。其次,我们使用简单的树匹配和回溯算法在DOM树之间创建最大匹配树。然后,通过分析最大匹配树中节点的特征,剔除噪声节点,生成提取模板;最后,我们为目标新闻页面构建一个依赖于模板的包装器,其结构与示例相似。实验结果表明,该方法对Web新闻内容提取是有效的,查准率和查全率的平均调和平均值达到了98.3%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automatic Web News Content Extraction Based on Similar Pages
Today most news pages are generated from some underlying structured source, so we think that template-dependent wrappers should be more suitable for them than template-independent wrappers. In this paper, we propose a novel automatic template-dependent Web news content extraction approach based on similar pages. Firstly, We choose two similar pages as training samples and represent them as two HTML DOM trees. Secondly, we create the maximum matching tree between the DOM trees using our simple tree matching and backtracking algorithm. Then, by analyzing the characteristics of nodes in the maximum matching tree, we eliminate the noise nodes to generate an extraction template. Finally, we build a template-dependent wrapper for target news pages whose structures are similar to the samples. Experimental results indicate that our approach is effective and efficient for Web news content extraction, and the average harmonic mean of precision and recall reaches 98.3% .
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