摘要
在传统的只统计人名用字的Naive Bayes分类算法的基础上,将人名上下文边界融入其中,并利用从大规模语料库中统计的人名用字、边界模板频率对人名定界,再通过扩散操作召回遗漏人名。该方法简单易行,并能取得很好的效果。实验结果表明,其F值达到了93.28%。
On the basis of the traditional Naive Bayesian classification algorithm that just considered character of Chinese person name,person name's up and down boundary words was brought in it. In order to overcome the difficulty of boundary defining,Chinese name 's character frequency and boundary templates ' frequency were counted from tagged corpus. Then these recognized person names are used to match the missed occurrence in the text. The method is easy and the final result is good. Experimental results show that the F-value for recognition of Chinese person name was increased.
出处
《科学技术与工程》
北大核心
2015年第6期83-86,98,共5页
Science Technology and Engineering
基金
国家自然科学基金(61363072)
教育部人文社科基金(11YJC740157
09YJC740027)
江西省自然科学基金(20114BAB201027)资助
关键词
NAIVE
BAYES
分类算法
边界模板
人名识别
Naive Bayesian classification algorithm
boundary templates
person name recognition