We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Identifying duplicate content using statistically improbable phrases.
- Authors
Errami, Mounir; Sun, Zhaohui; George, Angela C; Long, Tara C; Skinner, Michael A; Wren, Jonathan D; Garner, Harold R
- Abstract
Document similarity metrics such as PubMed's 'Find related articles' feature, which have been primarily used to identify studies with similar topics, can now also be used to detect duplicated or potentially plagiarized papers within literature reference databases. However, the CPU-intensive nature of document comparison has limited MEDLINE text similarity studies to the comparison of abstracts, which constitute only a small fraction of a publication's total text. Extending searches to include text archived by online search engines would drastically increase comparison ability. For large-scale studies, submitting short phrases encased in direct quotes to search engines for exact matches would be optimal for both individual queries and programmatic interfaces. We have derived a method of analyzing statistically improbable phrases (SIPs) for assistance in identifying duplicate content.
- Publication
Bioinformatics (Oxford, England), 2010, Vol 26, Issue 11, p1453
- ISSN
1367-4811
- Publication type
Journal Article
- DOI
10.1093/bioinformatics/btq146