基于LDA和SVM的新闻分类层次方法

Limeng Cui, Fan Meng, Yong Shi, Minqiang Li, An Liu
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引用次数: 20

摘要

在线数据的增长为用户提供了在互联网上获取信息的途径,但也为获取有价值的知识带来了挑战。本文对新闻文本进行分类,不仅对信息提供者组织和展示新闻有重要意义,而且对用户方便获取有价值的信息也有重要意义。提出了一种基于LDA和支持向量机的分层方法来完成这一任务,并进行了几个实验来评估我们的方法。结果表明,该方法在文本分类问题中具有较好的应用前景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Hierarchy Method Based on LDA and SVM for News Classification
He growth of the online data provides the user a access to information on the Internet but also creates the challenges to obtain the valuable knowledge. In this paper we focus on news text classification, which is meaningful for information provider to organize and display the news but also for the users to reach the valuable information easily. A hierarchy method based on LDA and SVM is proposed to accomplish this task and several experiments are conducted to evaluate our method. The results show that our method is promising in text classification problems.
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