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Proc AMIA Symp. 2001 : 319–323.
PMCID: PMC2243528

Automatic MeSH term assignment and quality assessment.

Abstract

For computational purposes documents or other objects are most often represented by a collection of individual attributes that may be strings or numbers. Such attributes are often called features and success in solving a given problem can depend critically on the nature of the features selected to represent documents. Feature selection has received considerable attention in the machine learning literature. In the area of document retrieval we refer to feature selection as indexing. Indexing has not traditionally been evaluated by the same methods used in machine learning feature selection. Here we show how indexing quality may be evaluated in a machine learning setting and apply this methodology to results of the Indexing Initiative at the National Library of Medicine.

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Selected References

These references are in PubMed. This may not be the complete list of references from this article.
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Articles from Proceedings of the AMIA Symposium are provided here courtesy of American Medical Informatics Association