Research of Protein Named Entity Recognition
Based on SVMs
Abstract: This paper describes an approach to identify protein named entity using Supports Vector Machines (SVMs), and selects four groups of features to do experiments for the protein corpus. Experiment results show the system performance of context features increases smaller than baseline system, and the combined feature of part of speech (POS) and word type is achieved 78.43% accuracy which is the best performance in all ex- periments. The research results show the combined feature of POS and word type play important roles in the protein entity recognition.
文章引用: 龚乐君 , 付亚星 , 孙啸 , 谢建明 , 于双鑫 (2011) 基于支持向量机的蛋白质命名实体识别的研究。 计算生物学， 1， 5-10. doi: 10.12677/hjcb.2011.12002
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