• DocumentCode
    3427608
  • Title

    Inferring grant support types from online biomedical articles

  • Author

    Kim, Jongwoo ; Le, Daniel X. ; Thoma, George R.

  • Author_Institution
    Nat. Libr. of Med., Bethesda, MD, USA
  • fYear
    2009
  • fDate
    2-5 Aug. 2009
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    The category of institution or organization underwriting the research reported in a scientific article is a required field (Grant Support type) in the bibliographic record of that article in the MEDLINEreg database. We describe a system based on a combination of a Naive Bayes classifier and heuristic rules that automatically infers the Grant Support types from article text. Testing the performance of the system on 2,000 biomedical articles shows Precision, Recall, and F-Measure exceeding 95%.
  • Keywords
    Bayes methods; bibliographic systems; classification; inference mechanisms; medical information systems; MEDLINE database; bibliographic record; grant support inference; naive Bayes classifier; online biomedical article; Abstracts; Databases; Entropy; Filters; Libraries; Machine learning; Machine learning algorithms; System testing; Text categorization; XML;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer-Based Medical Systems, 2009. CBMS 2009. 22nd IEEE International Symposium on
  • Conference_Location
    Albuquerque, NM
  • ISSN
    1063-7125
  • Print_ISBN
    978-1-4244-4879-1
  • Electronic_ISBN
    1063-7125
  • Type

    conf

  • DOI
    10.1109/CBMS.2009.5255359
  • Filename
    5255359