Items where author is affiliated with Ecole Polytechnique FÃ©dÃ©rale de Lausanne & Google ZÃ¼rich
Number of items: 1.
and Henzinger, Monika
and Marian, Ludmila
and Weber, Ingmar 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 classiï¬ed using their content , but a URL-only classiï¬er is preferable, (i) when speed is crucial, (ii) to enable content ï¬ltering before an (objectionable) web page is downloaded, (iii) when a pageâs 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 identiï¬cation task and evaluate its performance in extensive experiments on categorized web pages from the Open Directory Project (ODP). When training separate binary classiï¬ers 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 the task of classifying these pages into faculty, student, course and project pages, our methods improve over previous approaches by 13.8 points of F-measure.
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