Identifying duplicate content using statistically improbable phrases

Mounir Errami, Zhaohui Sun, Angela C. George, Tara C. Long, Michael A. Skinner, Jonathan D. Wren, Harold R. Garner

Research output: Contribution to journalArticle

15 Scopus citations

Abstract

Motivation: 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. Results: When applied to MEDLINE citations, this method substantially improves upon previous algorithms in the detection of duplication citations, yielding a precision and recall of 78.9% (versus 50.3% for eTBLAST) and 99.6% (versus 99.8% for eTBLAST), respectively.

Original languageEnglish (US)
Article numberbtq146
Pages (from-to)1453-1457
Number of pages5
JournalBioinformatics
Volume26
Issue number11
DOIs
StatePublished - May 13 2010

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

Fingerprint Dive into the research topics of 'Identifying duplicate content using statistically improbable phrases'. Together they form a unique fingerprint.

  • Cite this

    Errami, M., Sun, Z., George, A. C., Long, T. C., Skinner, M. A., Wren, J. D., & Garner, H. R. (2010). Identifying duplicate content using statistically improbable phrases. Bioinformatics, 26(11), 1453-1457. [btq146]. https://doi.org/10.1093/bioinformatics/btq146