A Comprehensive Study of Features and Algorithms for URL-based Topic Classification.

Eda Baykan, Monika Henzinger, Ludmila Marian, Ingmar Weber

    Veröffentlichungen: Beitrag in FachzeitschriftArtikelPeer Reviewed

    Abstract

    Given only the URL of a Web page, can we identify its topic? We study this problem in detail by exploring a large number of different feature sets and algorithms on several datasets. We also show that the inherent overlap between topics and the sparsity of the information in URLs makes this a very challenging problem. Web page classification without a page?s content is desirable when the content is not available at all, when a classification is needed before obtaining the content, or when classification speed is of utmost importance. For our experiments we used five different corpora comprising a total of about 3 million (URL, classification) pairs. We evaluated several techniques for feature generation and classification algorithms. The individual binary classifiers were then combined via boosting into metabinary classifiers. We achieve typical F-measure values between 80 and 85, and a typical precision of around 86. The precision can be pushed further over 90 while maintaining a typical level of recall between 30 and 40.
    OriginalspracheEnglisch
    Seiten (von - bis)1-29
    Seitenumfang29
    FachzeitschriftACM Transactions on the Web
    Jahrgang5
    Ausgabenummer3
    PublikationsstatusVeröffentlicht - 2011

    ÖFOS 2012

    • 1020 Informatik

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