As open-access policies gain acceptance, an increasing number of researchers are contributing their papers to publicly accessible web sites (i.e. self-archiving). Theoretically, these papers are accessible from standard search engines, but they tend to be obscured by other contents on the web. The purpose of this research is to develop a system that can automatically detect cademic articles and/or quasi-academic articles on the web. This paper describes experiments that were conducted on the performance of various classifiers and the results are compared in terms of precision, recall, and F-measure. The classifiers use attributes such as terms in PDF files and empirical rules. The results suggest the efficiency of a ranked output system which has several phases to identify academic articles.
|Number of pages||21|
|Journal||Library and Information Science|
|Publication status||Published - 2006|
All Science Journal Classification (ASJC) codes
- Library and Information Sciences