Exploring the boundaries: gene and protein identification in biomedical text.
BACKGROUND
Good automatic information extraction tools offer hope for automatic processing of the exploding biomedical literature, and successful named entity recognition is a key component for such tools.


METHODS
We present a maximum-entropy based system incorporating a diverse set of features for identifying gene and protein names in biomedical abstracts.


RESULTS
This system was entered in the BioCreative comparative evaluation and achieved a precision of 0.83 and recall of 0.84 in the "open" evaluation and a precision of 0.78 and recall of 0.85 in the "closed" evaluation.


CONCLUSIONS
Central contributions are rich use of features derived from the training data at multiple levels of granularity, a focus on correctly identifying entity boundaries, and the innovative use of several external knowledge sources including full MEDLINE abstracts and web searches.
Finkel
Jenny
J
Department of Computer Science, Stanford University, Stanford, CA 94305-9040, USA. jrfinkel@stanford.edu
Dingare
Shipra
S
Manning
Christopher D
CD
Nissim
Malvina
M
Alex
Beatrice
B
Grover
Claire
C
D035843:Biomedical Research / Q000145:classification* / Q000379:methods; D019295:Computational Biology / Q000145:classification / Q000379:methods; D005796:Genes; D016247:Information Storage and Retrieval / Q000145:classification / Q000379:methods; D008091:Literature; D011506:Proteins / Q000145:classification*; D009626:Terminology as Topic
D011506:Proteins