摘要
针对贸易文本区别于普通文本的不同特性,提出了基于贸易政策文本的主题挖掘模型,对世界贸易组织的贸易政策审议报告进行研究,归纳出文本的主要内容和主题变化趋势,为商务部和中国驻世贸组织使团提供有价值的信息辅助,从而使得快速有效的处理大量的文本成为可能。通过大量的实验,表明了主题挖掘模型的有效性。
The objective of text mining is to extract useful information from a large quantity of texts. In the big data era, it is of great significance to apply advanced machine learning algorithms on the traditional texts, to provide guidance and suggestions for the experts by extracting knowledge from texts. This paper proposes a topic mining model based on trade policy reviews of the World Trade Organization, to help experts grasp the main themes of the texts, and handle massive texts effectively and efficiently. The proposed algorithm is proved to be robust and effective through extensive experiments.
出处
《计算机工程与应用》
CSCD
北大核心
2016年第11期60-67,共8页
Computer Engineering and Applications
基金
教育部人文社会科学研究青年基金项目(No.13YJC630126)
教育部留学回国人员科研启动基金
上海高校智库上海对外经贸大学国际经贸治理与中国改革开放联合研究中心研究基金
