Abstract: 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.

Key words: trade policy review, text mining, machine learning

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。

关键词: 贸易政策审议, 文本挖掘, 机器学习