Format

Send to

Choose Destination
See comment in PubMed Commons below
BMC Bioinformatics. 2014 Feb 26;15:59. doi: 10.1186/1471-2105-15-59.

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

Author information

1
Computational Bioscience Program, U, of Colorado School of Medicine, Aurora, CO 80045, USA. christopher.funk@ucdenver.edu.

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 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.

PMID:
24571547
PMCID:
PMC4015610
DOI:
10.1186/1471-2105-15-59
[Indexed for MEDLINE]
Free PMC Article
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for BioMed Central Icon for PubMed Central
    Loading ...
    Support Center