维基百科超链接图的相关性和消歧研究
Studying the Wikipedia Hyperlink Graph for Relatedness and Disambiguation
摘要：
维基百科中的超链接和其他关系是一种特殊的资源，但仍然没有完全理解。在本文中，我们研究了维基百科中不同类型的链接，并将全图的使用与直接链接进行了对比。我们应用一个著名的随机游走算法在两个任务，词的相关性和命名实体消歧。我们表明，使用完整的图比直接链接更有效，非互惠链接会损害性能，并且类别和信息框没有任何好处，两项任务的结果都是一致的。我们为基于维基百科链接的系统设置了新的最先进的数字，与利用多个信息源和/或监督机器学习的系统相当。我们的方法是开源的，具有重现结果的指令，并且可以与基于文本的补充方法相结合。
英文摘要：
Hyperlinks and other relations in Wikipedia are a extraordinary resource which is still not fully understood. In this paper we study the different types of links in Wikipedia, and contrast the use of the full graph with respect to just direct links. We apply a well-known random walk algorithm on two tasks, word relatedness and named-entity disambiguation. We show that using the full graph is more effective than just direct links by a large margin, that non-reciprocal links harm performance, and that there is no benefit from categories and infoboxes, with coherent results on both tasks. We set new state-of-the-art figures for systems based on Wikipedia links, comparable to systems exploiting several information sources and/or supervised machine learning. Our approach is open source, with instruction to reproduce results, and amenable to be integrated with complementary text-based methods.
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。
