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. 2006:2006:1082.

Historical author affiliations assist verification of automatically generated MEDLINE citations

Tehseen F Sabir  1 , Susan E Hauser, George R Thoma
Affiliations

Historical author affiliations assist verification of automatically generated MEDLINE citations

Tehseen F Sabir et al. AMIA Annu Symp Proc. 2006.

Abstract

High OCR error rates encountered in author affiliations increase the manual labor needed to verify MEDLINE citations automatically created from scanned journal articles. This is due to poor OCR recognition of the small text and italics frequently used in printed affiliations. Using author-affiliation relationships found in existing MEDLINE records, the SeekAffiliation (SA) program automatically finds potentially correct and complete affiliations, thereby reducing manual effort and increasing the efficiency of creating the citations.

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References

    1. Thoma GR. Automating data entry into MEDLINE. Proc. 1999 Symp. on Document Image Understanding Technology; Apr 1999; College Park, MD: Institute for Advanced Computer Studies; pp. 217–8.
    1. Hauser SE, Sabir TF, Thoma GR. OCR correction using historical relationships from verified text in biomedical citations. Proc. 2003 Symp. on Document Image Understanding Technology; Apr 2003; College Park, MD: Institute for Advanced Computer Studies; pp. 171–7.
    1. U.S. National Institutes of Health, National Library of Medicine. Entrez Programming Utilities. http://eutils.ncbi.nlm.nih.gov/entrez/query/static/eutils_help.html.
    1. Hauser SE, Schlaifer J, Sabir TF, Demner-Fushman D, Thoma GR. Correcting OCR text by association with historic datasets. Proc. SPIE Electronic Imaging, January 2003. SPIE Vol. 5010; pp. 84–93.

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