Learning to Extract Content from News Webpages

Alex Spengler, P. Gallinari
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引用次数: 12

Abstract

We consider the problem of content extraction from online news webpages. To explore to what extent the syntactic markup and the visual structure of a webpage facilitate the extraction of its content, we compare two state-of-the-art classifiers as first instantiations of a general framework that allows for proper model comparison. To this end, we introduce the publicly available NEWS600 corpus, a set of 604 real world news webpages which have been annotated with 30 semantic labels. An empirical analysis of the two models on this dataset shows that the inclusion of structural information is indeed advantageous.
学习从新闻网页中提取内容
我们研究了在线新闻网页的内容抽取问题。为了探索网页的语法标记和视觉结构在多大程度上促进了其内容的提取,我们比较了两个最先进的分类器,作为允许适当模型比较的一般框架的第一个实例。为此,我们引入了公开的NEWS600语料库,这是一组604个真实世界的新闻网页,已经用30个语义标签进行了注释。对这两个模型在该数据集上的实证分析表明,包含结构信息确实是有利的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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