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We present a maximum-entropy based system incorporating a diverse set of features for identifying genes and proteins in biomedical abstracts. This system was entered in the BioCreative comparative eva...
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...
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 ...

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