Purely URL-based topic classification

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Purely URL-based topic classification
Given only the URL of a web page, can we identify its topic? This is the question that we examine in this paper. Usually, web pages are classified using their content [7], but a URL-only classifier is preferable, (i) when speed is crucial, (ii) to enable content filtering before an (objectionable) web page is downloaded, (iii) when a page's content is hidden in images, (iv) to annotate hyperlinks in a personalized web browser, without fetching the target page, and (v) when a focused crawler wants to infer the topic of a target page before devoting bandwidth to download it. We apply a machine learning approach to the topic identification task and evaluate its performance in extensive experiments on categorized web pages from the Open Directory Project (ODP). When training separate binary classifiers for each topic, we achieve typical F-measure values between 80 and 85, and a typical precision of around 85. We also ran experiments on a small data set of university web pages. For th...
Eda Baykan, Monika Rauch Henzinger, Ludmila Marian
Added 21 Nov 2009
Updated 21 Nov 2009
Type Conference
Year 2009
Where WWW
Authors Eda Baykan, Monika Rauch Henzinger, Ludmila Marian, Ingmar Weber
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