本文提出了一种支持向量机(SVM)和概率统计模型相结合的中国人名自动识别方法。该方法首先按字抽取特征向量的属性得到训练集,采用多项式核函数建立SVM人名识别模型,然后在特征空间中计算测试样本到SVM最优超平面的距离,当该距离大于给定的阈值时使用SVM对测试样本进行分类,否则使用概率统计方法。实验表明,采用混合模型,对样本在空间的不同分布使用不同的方法可以取得比单独使用SVM或概率统计更好的分类效果,系统开式综合指标F-值比单纯使用支持向量机方法提高了1.51%。

This paper describes a hybrid model and the corresponding algorithm combining support vector machines (SVM) with statistical methods to improve the performance of SVM for the task of Chinese person names recognition. In this algorithm, a training set is obtained by extracting the attributes of feature vectors based on characters and the SVM model of automatic identification of Chinese person names is set up by choosing a proper kernel function. Thus a threshold of the distance from the test sample to the hyperplane of SVM in feature space is used to separate SVM region and statistical method region. If the distance is greater than the given threshold, the test sample is classified using SVM; otherwise, the statistical model is used. The experimental results show the recall, precision and F-measure for recognition of Chinese person names based on the hybrid model are up to 91.96%, 94.62% and 93.27% respectively in open test. Compared with sole SVM, the F-measure increases 1.51%. By integrating the advantages of two methods, the performance is obviously improved.