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Large-scale biomedical concept recognition: an evaluation of current automatic annotators and their parameters.
- Source :
-
BMC Bioinformatics . 2014, Vol. 15 Issue 1, p1-47. 47p. - Publication Year :
- 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 systemontology pair are presented. Conclusions Baselines for concept recognition by three systems on eight biomedical ontologies are established (Fmeasures 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 14712105
- Volume :
- 15
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- BMC Bioinformatics
- Publication Type :
- Academic Journal
- Accession number :
- 94929281
- Full Text :
- https://doi.org/10.1186/1471-2105-15-59