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.