SVM与规则相结合的中文地名自动识别

在分析中文文本中地名特点的基础上,提出了一种支持向量机(SVM)与规则相结合的中文地名自动识别方法:按字抽取特征向量的属性,然后将这些属性转换成二进制向量并建立训练集,采用多项式Kernel函数,得到SVM识别地名的机器学习模型;通过对错误识别结果的分析,构建规则库对识别结果进行后处理,弥补了机器学习模型获取知识不够全面导致召回率偏低的不足。实验表明,用SVM与规则相结合的机制识别中文文本中的地名是有效的:系统开式召回率、精确率和F-值分别达89.57%、93.52%和91.50%。

Identifying Chinese Place Names Based on Support Vector Machines and Rules

By analyzing the characteristics of place names in Chinese texts,a method of automatic recognition of Chinese place names is presented,which combining support vector machines(SVMs) with rules.Firstly,feature vectors based on characters are extracted,and transferred into binary vectors.A training set is established,and the machine learning models for automatic identification of Chinese place names are obtained using polynomial kernel functions.Then,through careful error analysis,a rulebase is constructed and a post-processing step based on it is used,to overcome the shortcoming of low recall of machine learning model.The results show that the method is efficient for identifying Chinese place names.In open test,the recall,precision and F-measure reach 89.57%,93.52% and 91.50% respectively.