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A one-size-fits-all indexing method does not exist: automatic selection based on meta-learning.

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Jimeno-Yepes A, Mork J, Demner-Fushman D, Aronson AR
JCSE, vol. 6, no. 2, pp.151-160, 2012.
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).

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

Access the training and testing PMID lists used for this study at: http://ii.nlm.nih.gov/MTI_ML/index.shtml

Jimeno-Yepes A, Mork J, Demner-Fushman D, Aronson AR. A one-size-fits-all indexing method does not exist: automatic selection based on meta-learning. JCSE, vol. 6, no. 2, pp.151-160, 2012.