Centroid-based Classification Enhanced with Wikipedia

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI:10.1109/ICMLA.2010.17

Abdullah Bawakid, M. Oussalah

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引用次数: 3

Abstract

Most of the traditional text classification methods employ Bag of Words (BOW) approaches relying on the words frequencies existing within the training corpus and the testing documents. Recently, studies have examined using external knowledge to enrich the text representation of documents. Some have focused on using WordNet which suffers from different limitations including the available number of words, synsets and coverage. Other studies used different aspects of Wikipedia instead. Depending on the features being selected and evaluated and the external knowledge being used, a balance between recall, precision, noise reduction and information loss has to be applied. In this paper, we propose a new Centroid-based classification approach relying on Wikipedia to enrich the representation of documents through the use of Wikpedia’s concepts, categories structure, links, and articles text. We extract candidate concepts for each class with the help of Wikipedia and merge them with important features derived directly from the text documents. Different variations of the system were evaluated and the results show improvements in the performance of the system.

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基于质心的分类与维基百科增强

传统的文本分类方法大多采用词袋(BOW)方法，依赖于训练语料库和测试文档中存在的词频。近年来，有研究探讨了利用外部知识来丰富文档的文本表示。一些人专注于使用WordNet，它受到不同的限制，包括可用的单词数量、同义词集和覆盖范围。其他研究则使用了维基百科的不同方面。根据所选择和评估的特征以及所使用的外部知识，必须在召回率、精确度、降噪和信息损失之间取得平衡。在本文中，我们提出了一种新的基于中心点的分类方法，依靠维基百科通过使用维基百科的概念、分类结构、链接和文章文本来丰富文档的表示。我们在维基百科的帮助下为每个类提取候选概念，并将它们与直接从文本文档派生的重要特征合并。对系统的不同变化进行了评估，结果表明系统的性能有所改善。

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2010 Ninth International Conference on Machine Learning and Applications

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