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In this paper we present the evaluation
of a set of string similarity metrics used
to resolve the mapping from strings to
concepts in the UMLS MetaThesaurus.
String similarity is conceived as a single
component in a full Reference Resolution
System that would resolve such a mapping.
Given this qualification, we obtain
positive results achieving 73.6 F-measure
(76.1 precision and 71.4 recall) for the
task of assigning the correct UMLS concept
to a given string. Our results demonstrate
that adaptive string similarity methods
based on Conditional Random Fields
outperform standard metrics in this domain.