Fast webpage classification using URL features

  • Authors:
  • Min-Yen Kan;Hoang Oanh Nguyen Thi

  • Affiliations:
  • School of Computing, Singapore;School of Computing, Singapore

  • Venue:
  • Proceedings of the 14th ACM international conference on Information and knowledge management
  • Year:
  • 2005

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Abstract

We demonstrate the usefulness of the uniform resource locator (URL) alone in performing web page classification. This approach is faster than typical web page classification, as the pages do not have to be fetched and analyzed. Our approach segments the URL into meaningful chunks and adds component, sequential and orthographic features to model salient patterns. The resulting features are used in supervised maximum entropy modeling. We analyze our approach's effectiveness on two standardized domains. Our results show that in certain scenarios, URL-based methods approach the performance of current state-of-the-art full-text and link-based methods.