Quality assessment of Wikipedia articles without feature engineering

Quang-Vinh Dang, C. Ignat
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引用次数: 54

Abstract

As Wikipedia became the largest human knowledge repository, quality measurement of its articles received a lot of attention during the last decade. Most research efforts focused on classification of Wikipedia articles quality by using a different feature set. However, so far, no “golden feature set” was proposed. In this paper, we present a novel approach for classifying Wikipedia articles by analysing their content rather than by considering a feature set. Our approach uses recent techniques in natural language processing and deep learning, and achieved a comparable result with the state-of-the-art.
没有特征工程的维基百科文章的质量评估
随着维基百科成为最大的人类知识库,其文章的质量度量在过去十年中受到了很多关注。大多数的研究都是通过使用不同的特征集来对维基百科文章的质量进行分类。然而,到目前为止,还没有提出“黄金特性集”。在本文中,我们提出了一种通过分析维基百科文章的内容而不是考虑特征集来对其进行分类的新方法。我们的方法使用了自然语言处理和深度学习方面的最新技术,并取得了与最先进技术相当的结果。
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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