Mining Hidden Concepts: Using Short Text Clustering and Wikipedia Knowledge

Cheng-Lin Yang, Nuttakorn Benjamasutin, Y. Chen-Burger
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引用次数: 4

Abstract

In recent years, there has been a rapidly increasing use of social networking platforms in the forms of short-text communication. However, due to the short-length of the texts used, the precise meaning and context of these texts are often ambiguous. To address this problem, we have devised a new community mining approach that is an adaptation and extension of text clustering, using Wikipedia as background knowledge. Based on this method, we are able to achieve a high level of precision in identifying the context of communication. Using the same methods, we are also able to efficiently identify hidden concepts in Twitter texts. Using Wikipedia as background knowledge considerably improved the performance of short text clustering.
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挖掘隐藏概念:使用短文本聚类和维基百科知识
近年来,人们越来越多地使用社交网络平台进行短信交流。然而,由于所使用的文本长度较短,这些文本的确切含义和上下文往往是模棱两可的。为了解决这个问题,我们设计了一种新的社区挖掘方法,它是文本聚类的适应和扩展,使用维基百科作为背景知识。基于这种方法,我们能够在识别通信上下文方面达到很高的精度。使用相同的方法,我们还能够有效地识别Twitter文本中的隐藏概念。使用Wikipedia作为背景知识大大提高了短文本聚类的性能。
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