Forum topic detection based on hierarchical clustering

Hui Li, Qing Li
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引用次数: 4

Abstract

Forum has become one of the main platforms for people to express their personal point of view, with a lot of information surging in the forum everyday. How to detect automatically a forum topic among the massive information becomes an important and hard task. Though there are plenty of studies for topic detection, it is still a challenge to make it fast and accurately. This paper introduces the principle of maximum entropy and information gain when calculating feature weight. Our algorithm is based on the agglomerative hierarchical clustering (AHC). Experiments are focused on a game forum and handling sparse forum short texts. The result shows that the improved method can detect the forum topic more effectively.
基于层次聚类的论坛主题检测
论坛已经成为人们表达个人观点的主要平台之一,每天都有大量的信息在论坛上涌动。如何从海量信息中自动检测出一个论坛主题,成为一项重要而艰巨的任务。虽然关于话题检测的研究很多,但如何快速准确地进行话题检测仍然是一个挑战。本文介绍了最大熵和信息增益在计算特征权重时的原理。该算法基于聚类层次聚类(AHC)。实验集中在一个游戏论坛和处理稀疏的论坛短文本。结果表明,改进后的方法可以更有效地检测论坛主题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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