Journal article

Large-scale biomedical concept recognition: an evaluation of current automatic annotators and their parameters

Christopher Funk, William Baumgartner, Benjamin Garcia, Christophe Roeder, Michael Bada, K Bretonnel Cohen, Lawrence E Hunter, Karin Verspoor

BMC Bioinformatics | BMC | Published : 2014

Abstract

BACKGROUND: Ontological concepts are useful for many different biomedical tasks. Concepts are difficult to recognize in text due to a disconnect between what is captured in an ontology and how the concepts are expressed in text. There are many recognizers for specific ontologies, but a general approach for concept recognition is an open problem. RESULTS: Three dictionary-based systems (MetaMap, NCBO Annotator, and ConceptMapper) are evaluated on eight biomedical ontologies in the Colorado Richly Annotated Full-Text (CRAFT) Corpus. Over 1,000 parameter combinations are examined, and best-performing parameters for each system-ontology pair are presented. CONCLUSIONS: Baselines for concept reco..

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Grants

Awarded by NIH


Awarded by NSF


Awarded by NATIONAL LIBRARY OF MEDICINE


Awarded by Div Of Biological Infrastructure


Funding Acknowledgements

This work was supported by NIH grant 2T15LM009451 to LEH and NSF grant DBI-0965616 to KV. KV was also supported by National ICT Australia (NICTA). NICTA is funded by the Australian Government as represented by the Department of Broadband, Communications and the Digital Economy and the Australian Research Council through the ICT Centre of Excellence program. We would like to acknowledge Helen Johnson and Cesar Mejia Munoz for executing early versions of these experiments. We also appreciate Willie Rogers from NLM with help getting MetaMap to work with ontologies other than those in the UMLS.