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JCSE, vol. 6, no. 2, pp.151-160, June, 2012


A One-Size-Fits-All Indexing Method Does Not Exist: Automatic Selection Based on Meta-Learning

Antonio Jimeno-Yepes, James G. Mork, Dina Demner-Fushman, Alan R. Aronson
National Library of Medicine, Bethesda, MD, USA

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

Keyword: MeSH; MEDLINE; Text categorization; Automatic indexing; Meta-learning

Full Paper:   101 Downloads, 5925 View

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