摘要
近年来,我国能源行业的安全生产责任事故层出不穷。为了能将相关事故尽可能快速有效地汇报相关部门,必须对互联网上关于能源行业的事故新闻做到实时的监控。该文提出了一套对互联网上能源行业事故实时抓取分析的事故监控系统,采用机器学习与规则匹配相结合的文本挖掘技术来分类新闻事件,采用改进后的新闻事件分类方法比单纯使用机器学习的方法在准确率和召回率都有显著提升。文章最后提出系统实现的框架,并提出改进意见。
In recent years, Chinese production safety of energy industry accidents occur frequently.In order to report the related incidents to relevant departments quickly and efficiently, we must achieve real-time monitoring to accident news of energy industry on the Internet. This paper presents an Accident Monitoring System to crawl and analysis the accidents of energy industry on the Internet in real time,this system uses a combination of machine learning and rule matching text mining techniques to extract news events, Comparing with the machine learning, this method can significantly improved precision and recall rates. Finally,the paper proposes a framework for system implementation, and suggests improvements.
出处
《电脑知识与技术》
2016年第1X期61-64,共4页
Computer Knowledge and Technology
关键词
文本表示
特征提取
SVM分类器
规则过滤
text representation
feature extraction
SVM classifier
rule filter
