Biomedical named entity recognition system

We propose a machine learning approach, using a Maximum Entropy (ME) model to construct a Named Entity Recognition (NER) classifier to retrieve biomedical names from texts. In experiments, we utilize a blend of various linguistic features incorporated into the ME model to assign class labels and location within an entity sequence, and a postprocessing strategy for corrections to sequences of tags to produce a state of the art solution. The experimental results on the GENIA corpus achieved an F-score of 68.2% for semantic classification of 23 categories and achieved F-score of 78.1% on identification.

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