Clustering web search results using Wikipedia resource

Chung-Nguyen Tran, A. Ameljanczyk
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引用次数: 1

Abstract

The paper presents a proposal of a new method for clustering search results. The method uses an external knowledge resource, which can be, for example, Wikipedia. Wikipedia – the largest encyclopedia, is a free and popular knowledge resource which is used to extract topics from short texts. Similarities between documents are calculated based on the similarities between these topics. After that, affinity propagation clustering algorithm is employed to cluster web search results. Proposed method is tested by AMBIENT dataset and evaluated within the experimental framework provided by a SemEval-2013 task. The paper also suggests new method to compare global performance of algorithms using multi – criteria analysis.
使用维基百科资源聚类网络搜索结果
本文提出了一种新的搜索结果聚类方法。该方法使用外部知识资源,例如维基百科。维基百科-最大的百科全书,是一个免费和流行的知识资源,用于从短文本中提取主题。文档之间的相似度是基于这些主题之间的相似度来计算的。然后,采用亲和传播聚类算法对web搜索结果进行聚类。提出的方法通过AMBIENT数据集进行测试,并在SemEval-2013任务提供的实验框架内进行评估。本文还提出了一种利用多准则分析比较算法全局性能的新方法。
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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