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基于精简特征集和融合新特征的基因名识别
引用本文:张仁军,邵峰晶,孙仁诚.基于精简特征集和融合新特征的基因名识别[J].青岛大学学报(自然科学版),2014(2):61-64,89.
作者姓名:张仁军  邵峰晶  孙仁诚
作者单位:青岛大学信息工程学院,青岛266071
基金项目:国家自然基金重大研究计划培育项目(批准号:91130035)资助;山东省自然科学基金重点项目(批准号:ZR2012FZ003)资助;山东省自然科学基金青年基金(批准号:ZR2012FQO17)资助.
摘    要:根据生物医学文本中基因名的特点,提出了一组新特征用于基因名的识别。利用精简的特征集,将提出的新特征融合进精简特征集中。应用GlobalLinear模型和感知机学习算法在BioCreativeⅡ数据集中对提出的方法进行了验证,结果表明,通过使用数量较少的、区分能力强的特征,仍能使系统达到较高的性能。当融合新特征时,系统的精确率和召回率也有一定的提高。

关 键 词:基因名识别  精简特征集  权值向量  学习算法

Identifying Gene Names Using Reductive Features and New Features
ZHANG Ren-jun,SHAO Feng-jing,SUN Ren-cheng.Identifying Gene Names Using Reductive Features and New Features[J].Journal of Qingdao University(Natural Science Edition),2014(2):61-64,89.
Authors:ZHANG Ren-jun  SHAO Feng-jing  SUN Ren-cheng
Institution:(College of Information Engineering, Qingdao University, Qingdao 266071, China)
Abstract:Based on the features in biomedical text, a new feature method was proposed to recognize gene names. A reductive feature set combined with some new features was employed in the form of gene lexi- cons, applying the method on BioCreative Ⅱ shared dataset with global linear framework and perceptron learning algorithm. Results of the experiment show that in the case of reductive and strong classification features, the system still obtain high performance. When incorporate new features, the precision and recall continue improved to some extent.
Keywords:gene name recognition  reductive feature  weight vector  learning algorithm
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