creators_name: Baykan, Eda creators_name: Henzinger, Monika creators_name: Marian, Ludmila creators_name: Weber, Ingmar type: conference_item datestamp: 2009-04-06 19:12:47 lastmod: 2009-04-07 14:02:55 metadata_visibility: show title: Purely URL-based Topic Classification ispublished: pub full_text_status: public pres_type: poster abstract: 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 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. date: 2009-04 pagerange: 1109-1109 event_title: 18th International World Wide Web Conference event_location: Madrid, Spain event_dates: April 20th-24th, 2009 event_type: conference refereed: TRUE citation: Baykan, Eda and Henzinger, Monika and Marian, Ludmila and Weber, Ingmar (2009) Purely URL-based Topic Classification. In: 18th International World Wide Web Conference, April 20th-24th, 2009, Madrid, Spain. document_url: http://www2009.eprints.org/135/1/p1109.pdf