---
_id: '11673'
abstract:
- lang: eng
  text: 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.
article_number: '15'
article_processing_charge: No
article_type: original
author:
- first_name: Eda
  full_name: Baykan, Eda
  last_name: Baykan
- first_name: Monika H
  full_name: Henzinger, Monika H
  id: 540c9bbd-f2de-11ec-812d-d04a5be85630
  last_name: Henzinger
  orcid: 0000-0002-5008-6530
- first_name: Ludmila
  full_name: Marian, Ludmila
  last_name: Marian
- first_name: Ingmar
  full_name: Weber, Ingmar
  last_name: Weber
citation:
  ama: Baykan E, Henzinger MH, Marian L, Weber I. A comprehensive study of features
    and algorithms for URL-based topic classification. <i>ACM Transactions on the
    Web</i>. 2011;5(3). doi:<a href="https://doi.org/10.1145/1993053.1993057">10.1145/1993053.1993057</a>
  apa: Baykan, E., Henzinger, M. H., Marian, L., &#38; Weber, I. (2011). A comprehensive
    study of features and algorithms for URL-based topic classification. <i>ACM Transactions
    on the Web</i>. Association for Computing Machinery. <a href="https://doi.org/10.1145/1993053.1993057">https://doi.org/10.1145/1993053.1993057</a>
  chicago: Baykan, Eda, Monika H Henzinger, Ludmila Marian, and Ingmar Weber. “A Comprehensive
    Study of Features and Algorithms for URL-Based Topic Classification.” <i>ACM Transactions
    on the Web</i>. Association for Computing Machinery, 2011. <a href="https://doi.org/10.1145/1993053.1993057">https://doi.org/10.1145/1993053.1993057</a>.
  ieee: E. Baykan, M. H. Henzinger, L. Marian, and I. Weber, “A comprehensive study
    of features and algorithms for URL-based topic classification,” <i>ACM Transactions
    on the Web</i>, vol. 5, no. 3. Association for Computing Machinery, 2011.
  ista: Baykan E, Henzinger MH, Marian L, Weber I. 2011. A comprehensive study of
    features and algorithms for URL-based topic classification. ACM Transactions on
    the Web. 5(3), 15.
  mla: Baykan, Eda, et al. “A Comprehensive Study of Features and Algorithms for URL-Based
    Topic Classification.” <i>ACM Transactions on the Web</i>, vol. 5, no. 3, 15,
    Association for Computing Machinery, 2011, doi:<a href="https://doi.org/10.1145/1993053.1993057">10.1145/1993053.1993057</a>.
  short: E. Baykan, M.H. Henzinger, L. Marian, I. Weber, ACM Transactions on the Web
    5 (2011).
date_created: 2022-07-27T13:48:11Z
date_published: 2011-07-01T00:00:00Z
date_updated: 2022-09-12T08:46:56Z
day: '01'
doi: 10.1145/1993053.1993057
extern: '1'
intvolume: '         5'
issue: '3'
keyword:
- Topic classification
- URL
- ODP
language:
- iso: eng
month: '07'
oa_version: None
publication: ACM Transactions on the Web
publication_identifier:
  eissn:
  - 1559-114X
  issn:
  - 1559-1131
publication_status: published
publisher: Association for Computing Machinery
quality_controlled: '1'
scopus_import: '1'
status: public
title: A comprehensive study of features and algorithms for URL-based topic classification
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 5
year: '2011'
...