基于内容和时间相似度相结合的热点话题检测

Yi Zhao, Kun Zhang, Hong Zhang, Xia Yan, Ying Cai
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引用次数: 1

摘要

热点话题检测一直是一个热门的研究领域,该技术在现实生活中有大量的应用。然而,以往的工作大多只关注新闻本身的文本信息,而忽略了新闻的其他属性,比如新闻发布的时间,这些属性也可以从它的角度来说明所描述的话题。而其他方法只使用一种方法来计算文本相似度,这些方法都有其缺点。为了解决这些问题,我们提出了自己的主题检测算法,该算法考虑到标题和文本之间的信息差异,结合几种方法计算文本相似度,并将文本和时间相似度结合在一起。我们测试了组合相似度计算方法,并测试了几个时间相似方程的效果。然后采用线性模型、二次多项式模型和神经网络模型三种不同的模型计算组合相似度。最后给出了实验结果和分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hot topic detection based on combined content and time similarity
Hot topic detection has always been a hot research field, and there are a large number of the applications of this technology in real life. Most of the previous work, however, focused only on the textual information of the news itself, while ignoring the other attributes of the news, such as the time the news was published, which can also tell the topic described in its perspective. And others use only one certain method to calculate the text similarity, which all have their disadvantages. To solve these problems, we proposed our own topic detection algorithm, which takes into account the information difference between the title and the text, combines several methods to calculate text similarity, and combines text and time similarity together. We tested the combined similarity calculation methods, and tested the effect of several time similarity equations. Then we took three different models to calculate the combined similarity which are linear model, quadratic polynomial model and neural network model. Finally, we give out the results and analysis of our experiments.
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