Automated extraction of non <h>-tagged headers in webpages by decision trees

Author

Okada, Hidehiko ; Arakawa, Hiroki

Author_Institution

Grad. Sch. of Eng., Kyoto Sangyo Univ., Kyoto, Japan

fYear

2011

fDate

13-18 Sept. 2011

Firstpage

2117

Lastpage

2120

Abstract

The guideline #5.2a in the JIS X 8341-3 reccomends to “represent headings with heading elements instead of difference in font size, etc”. Thus, in checking webpage accessibility, headers that are not tagged with heading tags (<;h1>;-<;h6>;) should be extracted as problems. In this paper, we propose a method for the extraction. Our idea is to let a machine learning method to automatically derive extraction rules from problem instances on the web. We define 26 attributes of HTML elements for deriving the rules. Values of these attributes are calculated by parsing the HTML source of the webpage. Accuracy of our method was evaluated by 10-fold cross validations with the data we collected from the web. The accuracy was 85-88% in average in terms of F-measure. Non <;h>;-tagged image headers were slightly better discriminated than non <;h>;-tagged text headers.

Keywords

Internet; decision trees; hypermedia markup languages; learning (artificial intelligence); program compilers; text analysis; 10-fold cross validation; F measure; HTML element; HTML source parsing; JIS X 8341-3; Web page; automated extraction rule; decision tree; heading element; heading tag; machine learning method; non <;h>;-tagged text header; non<;h>;tagged image header; Accuracy; Decision trees; Guidelines; HTML; Learning systems; Machine learning; Vegetation; Web accessibility; automated checking; decision tree; heading; machine learning;

fLanguage

English

Publisher

ieee

Conference_Titel

SICE Annual Conference (SICE), 2011 Proceedings of

Conference_Location

Tokyo

ISSN

pending

Print_ISBN

978-1-4577-0714-8

Type

conf

Filename

6060321