Computer Science

Web mediators for accessible browsing

Document Type

Conference Paper

Abstract

We present a highly accurate method for classifying web pages based on link percentage, which is the percentage of text characters that are parts of links normalized by the number of all text characters on a web page. We also present a novel link grouping algorithm using agglomerative hierarchical clustering that groups links in the same spatial neighborhood together while preserving link structure. Grouping allows users with severe disabilities to use a scan-based mechanism to tab through a web page and select items. In experiments, we saw up to a 40-fold reduction in the number of commands needed to click on a link with a scan-based interface. Our classification method consistently outperformed a baseline classifier even when using minimal data to generate article and index clusters, and achieved classification accuracy of 94.0% on web sites with well-formed or slightly malformed HTML, compared with 80.1% accuracy for the baseline classifier. © Springer-Verlag Berlin Heidelberg 2007.

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Publication Date

2007

Volume

4397 LNCS

First Page

447

Last Page

466

ISSN

0302-9743

ISBN

9783540710240

DOI

10.1007/978-3-540-71025-7_29

Keywords

K-means clustering, Link grouping, Web mediators, Web page classification

APA Citation

Waber, B. N., Magee, J. J., & Betke, M. (2007). Web mediators for accessible browsing. In Universal Access in Ambient Intelligence Environments: 9th ERCIM Workshop on User Interfaces for All, Königswinter, Germany, September 27-28, 2006. Revised Papers (pp. 447-466). Springer Berlin Heidelberg.

Link to Full Text

Share

COinS