TY  - CONF
ID  - www2009135
UR  - http://www2009.eprints.org/135/
A1  - Baykan, Eda
A1  - Henzinger, Monika
A1  - Marian, Ludmila
A1  - Weber, Ingmar
Y1  - 2009/04//
N2  - 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 classi?ed using their content [7], but a URL-only classi?er is preferable, (i) when speed is crucial, (ii) to enable content ?ltering 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 identi?cation task and evaluate its performance in extensive experiments on categorized web pages from the Open Directory Project (ODP). When training separate binary classi?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.
TI  - Purely URL-based Topic Classification
SP  - 1109
M2  - Madrid, Spain
AV  - public
EP  - 1109
T2  - 18th International World Wide Web Conference
ER  -