We propose a cascaded approach for extracting biomedical named entities from text documents using a unified model. Previous works often ignore the high computational cost incurred by a single-phase approach. We alleviate this problem by dividing the named entity extraction task into a segmentation task and a classification task, reducing the computational cost by an order of magnitude. A unified model, which we term "maximum-entropy margin-based" (MEMB), is used in both tasks. The MEMB model considers the error between a correct and an incorrect output during training and helps improve the performance of extracting sparse entity types that occur in biomedical literature. We report experimental evaluations on the GENIA corpus available from the BioNLP/NLPBA (2004) shared task, which demonstrate the state-of-the-art performance achieved by the proposed approach.
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