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LREC
2008

Automatic Extraction of Textual Elements from News Web Pages

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Automatic Extraction of Textual Elements from News Web Pages
In this paper we present an algorithm for automatic extraction of textual elements, namely titles and full text, associated with news stories in news web pages. We propose a supervised machine learning classification technique based on the use of a Support Vector Machine (SVM) classifier to extract the desired textual elements. The technique uses internal structural features of a webpage without relying on the Document Object Model to which many content authors fail to adhere. The classifier uses a set of features which rely on the length of text, the percentage of hypertext, etc. The resulting classifier is nearly perfect on previously unseen news pages from different sites. The proposed technique is successfully employed in Alzoa.com, which is the largest Arabic news aggregator on the web.
Hossam Ibrahim, Kareem Darwish, Abdel-Rahim Madany
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2008
Where LREC
Authors Hossam Ibrahim, Kareem Darwish, Abdel-Rahim Madany
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