Adaptive string similarity metrics for biomedical reference resolution

Authors:
Ben Wellner;José Castaño;James Pustejovsky
Affiliations:
Brandeis University, Waltham, MA and The MITRE Corporation, Bedford, MA;Brandeis University, Waltham, MA;Brandeis University, Waltham, MA
Venue:
ISMB '05 Proceedings of the ACL-ISMB Workshop on Linking Biological Literature, Ontologies and Databases: Mining Biological Semantics
Year:
2005

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Abstract

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.