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
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;
Conference_Titel :
Computer-Based Medical Systems, 2009. CBMS 2009. 22nd IEEE International Symposium on
Conference_Location :
Albuquerque, NM
Print_ISBN :
978-1-4244-4879-1
Electronic_ISBN :
1063-7125
DOI :
10.1109/CBMS.2009.5255359
