<p>This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p> <p>We present a maximum entropy-based system for identifying named entities (NEs) in
biomedical abstracts and present its performance in the only two biomedical named
entity recognition (NER) comparative evaluations that have been held to date, namely
BioCreative and Coling BioNLP. Our system obtained an exact match F-score of
83.2% in the BioCreative evaluation and 70.1% in the BioNLP evaluation. We discuss
our system in detail, including its rich use of local features, attention to correct
boundary identification, innovative use of external knowledge resources, including
parsing and web searches, and rapid adaptation to new NE sets. We also discuss
in depth problems with data annotation in the evaluations which caused the final
performance to be lower than optimal.</p>
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Comp Funct Genomics. 2005 Feb-Mar; 6(1-2):77-85