Library and Information Science Paper (ID LIS056043)
- AGATA,Teru, IKEUCHI,Atsushi, ISHIDA,Emi, NOZUE,Michiko, KUNO,Takashi, UEDA,Shuichi
- Automatic identification of academic articles in Japanese PDF files
- No.56, p.43-63
- Issue date
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 academic 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.
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