Journal
BMC BIOINFORMATICS
Volume 15, Issue -, Pages -Publisher
BMC
DOI: 10.1186/1471-2105-15-59
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Funding
- NIH [2T15LM009451]
- NSF [0965616]
- National ICT Australia (NICTA)
- Department of Broadband, Communications and the Digital Economy
- Australian Research Council through the ICT Centre of Excellence program
- Direct For Biological Sciences
- Div Of Biological Infrastructure [0965616] Funding Source: National Science Foundation
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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 recognition by three systems on eight biomedical ontologies are established (F-measures range from 0.14-0.83). Out of the three systems we tested, ConceptMapper is generally the best-performing system; it produces the highest F-measure of seven out of eight ontologies. Default parameters are not ideal for most systems on most ontologies; by changing parameters F-measure can be increased by up to 0.4. Not only are best performing parameters presented, but suggestions for choosing the best parameters based on ontology characteristics are presented.
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