Journal of Computing Science and Engineering

Korean Institute of Information Scientists and Engineers (한국정보과학회)
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A One-Size-Fits-All Indexing Method Does Not Exist: Automatic Selection Based on Meta-Learning

  • Jimeno-Yepes, Antonio (National Library of Medicine) ;
  • Mork, James G. (National Library of Medicine) ;
  • Demner-Fushman, Dina (National Library of Medicine) ;
  • Aronson, Alan R. (National Library of Medicine)
  • Received : 2012.02.18
  • Accepted : 2012.03.18
  • Published : 2012.06.30
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Abstract

We present a methodology that automatically selects indexing algorithms for each heading in Medical Subject Headings (MeSH), National Library of Medicine's vocabulary for indexing MEDLINE. While manually comparing indexing methods is manageable with a limited number of MeSH headings, a large number of them make automation of this selection desirable. Results show that this process can be automated, based on previously indexed MEDLINE citations. We find that AdaBoostM1 is better suited to index a group of MeSH hedings named Check Tags, and helps improve the micro F-measure from 0.5385 to 0.7157, and the macro F-measure from 0.4123 to 0.5387 (both p < 0.01).

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