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arXiv 2103.00068cs.CY
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维基百科的不可知论主题分类

Language-agnostic Topic Classification for Wikipedia

Isaac Johnson, Martin Gerlach, Diego Sáez-Trumper

摘要:

对维基百科动态的许多分析的一个主要挑战-例如,内容质量的不平衡,受欢迎内容的地理差异,什么类型的文章吸引更多的编辑讨论--正在将非常多样化的维基百科文章组合成连贯一致的主题。这个问题已使用基于维基百科类别网络、维基专题和外部分类法的各种方法得到解决。然而,这些方法的覆盖范围一直有限:通常,只有一小部分文章可以被分类,或者该方法不能应用于维基百科上的300多种语言。在本文中,我们提出了一种语言不可知的方法,在一个条目中的链接的基础上分类的文章到一个分类的主题,可以很容易地应用到(几乎)任何语言和维基百科上的文章。我们表明,它匹配的语言依赖的方法的性能,同时更简单,有更大的覆盖面。

英文摘要:

A major challenge for many analyses of Wikipedia dynamics -- e.g., imbalances in content quality, geographic differences in what content is popular, what types of articles attract more editor discussion -- is grouping the very diverse range of Wikipedia articles into coherent, consistent topics. This problem has been addressed using various approaches based on Wikipedia's category network, WikiProjects, and external taxonomies. However, these approaches have always been limited in their coverage: typically, only a small subset of articles can be classified, or the method cannot be applied across (the more than 300) languages on Wikipedia. In this paper, we propose a language-agnostic approach based on the links in an article for classifying articles into a taxonomy of topics that can be easily applied to (almost) any language and article on Wikipedia. We show that it matches the performance of a language-dependent approach while being simpler and having much greater coverage.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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