A dynamic hot topic extraction model based on time window

被引：0
|
作者：
Ma H. [1 ,2 ]
Yin M. [3 ]
He Q. [1 ]
Shi Z. [1 ]
机构：
[1] The Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences
[2] Graduate University of Chinese Academy of Sciences
[3] Internet Service Lab, Lenovo Corporate Research
来源：
Gaojishu Tongxin/Chinese High Technology Letters | 2010年 / 20卷 / 06期
关键词：
Burst; Pervasiveness; TF-PDF; Time window; Topic extraction;
D O I：
10.3772/j.issn.1002-0470.2010.06.007
中图分类号：
学科分类号：
摘要：
This paper gives a description of a study of topic organization in the news domain, and presents a novel dynamic hot topic extraction model based on the time window. The model combines two characteristics of hot topics together. One is the pervasiveness of topic terms in news texts, which is evaluated by the occurrences of the topic terms reported by different channels, and the more frequent the occurrence of the topic terms reported, the higher the pervasiveness of topic terms. The other one is the burst of topic terms in the news stream, which can be assessed by the abnormal occurrence frequencies of topic terms in a specific interval compared with other different time intervals. The time window is introduced to make burst detection and the term frequency-proportional document frequency (TF-PDF) is combined to weigh the terms. The experimental results demonstrate that this model is effective in topic extraction for news texts.
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页码：590 / 595
页数：5
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