Article

Combining link-based and content-based methods for web document classification

Authors:

Berthier Ribeiro-Neto,

Marcos André GonçalvesAuthors Info & Claims

CIKM '03: Proceedings of the twelfth international conference on Information and knowledge management

Pages 394 - 401

https://doi.org/10.1145/956863.956938

Published: 03 November 2003 Publication History

Abstract

This paper studies how link information can be used to improve classification results for Web collections. We evaluate four different measures of subject similarity, derived from the Web link structure, and determine how accurate they are in predicting document categories. Using a Bayesian network model, we combine these measures with the results obtained by traditional content-based classifiers. Experiments on a Web directory show that best results are achieved when links from pages outside the directory are considered. Link information alone is able to obtain gains of up to 46 points in F₁, when compared to a traditional content-based classifier. The combination with content-based methods can further improve the results, but too much noise may be introduced, since the text of Web pages is a much less reliable source of information. This work provides an important insight on which measures derived from links are more appropriate to compare Web documents and how these measures can be combined with content-based algorithms to improve the effectiveness of Web classification.

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Index Terms

Combining link-based and content-based methods for web document classification
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis
2. Information systems
  1. Information retrieval

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Published In

cover image ACM Conferences

CIKM '03: Proceedings of the twelfth international conference on Information and knowledge management

November 2003

592 pages

ISBN:1581137230

DOI:10.1145/956863

General Chair:
Donald Kraft
Louisiana State University
,
Program Chairs:
Ophir Frieder
Illinois Institute of Technology
,
Joachim Hammer
University of Florida
,
Sajda Qureshi
University of Nebraska, Omaha
,
Len Seligman
The MITRE Corporation

Copyright © 2003 ACM.

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Published: 03 November 2003

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https://doi.org/10.2298/CSIS210211057K
Zhang ZSu MBao ZLiu XYao YSu S(2022)Identifying Categories of Domain Names by Using Deep Learning Methods2022 International Conference on Machine Learning, Cloud Computing and Intelligent Mining (MLCCIM)10.1109/MLCCIM55934.2022.00092(504-510)Online publication date: Aug-2022
https://doi.org/10.1109/MLCCIM55934.2022.00092
Khalid SKhusro SUllah IDawson-Amoah G(2019)On The Current State of Scholarly Retrieval SystemsEngineering, Technology & Applied Science Research10.48084/etasr.24489:1(3863-3870)Online publication date: 16-Feb-2019
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