Abstract
BackgroundOntological 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.ResultsThree 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.ConclusionsBaselines 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.
Highlights
Ontological concepts are useful for many different biomedical tasks
Analysis of results files For each ontology-system pair, an analysis was performed on the maximum F-measure parameter combination
This provides a synopsis of overall performance for each system with comments about common terms correct (TPs), errors (FPs), and categories missed (FNs)
Summary
Ontological concepts are useful for many different biomedical tasks. There are many recognizers for specific ontologies, but a general approach for concept recognition is an open problem. Ontologies have played an important role in the development of natural language processing systems in the biomedical domain, which can use ontologies both as terminological resources and as resources that provide important semantic constraints on biological entities and events [4]. Ontologies provide such systems with a target conceptual representation that abstracts over variations in the surface realization of terms. They note that a limitation of their approach is the dependency on tools that establish linkages between ontology concepts and their textual representations
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