Identifying duplicate content using statistically improbable phrases.
BACKGROUND
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.


BACKGROUND
Similar citations identified by this work are freely accessible in the Déjà vu database, under the SIP discovery method category at http://dejavu.vbi.vt.edu/dejavu/.
Errami
Mounir
M
Division of Translational Research, The University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX 75290-9185, USA. merrami@collin.edu
Sun
Zhaohui
Z
George
Angela C
AC
Long
Tara C
TC
Skinner
Michael A
MA
Wren
Jonathan D
JD
Garner
Harold R
HR
D000043:Abstracting and Indexing / Q000379:methods*; D016208:Databases, Factual; D015713:Duplicate Publications as Topic; D016239:MEDLINE; D015714:Plagiarism; D039781:PubMed; D057386:Search Engine; D014481:United States
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