基于维基百科超链接网络的语义相关性度量研究

Fei-yue Ye, Feng Zhang
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引用次数: 7

摘要

维基百科作为一个知识覆盖面大、语义信息丰富、更新速度快的免费在线百科全书,为语义相关性的度量带来了新的思路。本文提出了一种通过挖掘维基百科中存在的丰富的语义信息来度量词之间语义相关性的新方法。与以往仅基于页面网络或类别网络计算语义相关性的方法不同,我们的方法不仅考虑了页面网络的语义信息,还结合了类别网络的语义信息,提高了结果的准确性。此外,在同一测试集上,将算法的计算结果与著名知识库(如Hownet)和基于Wikipedia的传统方法进行比较,对算法进行了分析和评价,证明了算法的优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on measuring semantic correlation based on the Wikipedia hyperlink network
As a free online encyclopedia with a large-scale of knowledge coverage, rich semantic information and quick update speed, Wikipedia brings new ideas to measure semantic correlation. In this paper, we present a new method for measuring the semantic correlation between words by mining rich semantic information that exists in Wikipedia. Unlike the previous methods that calculate semantic relatedness merely based on the page network or the category network, our method not only takes into account the semantic information of the page network, also combines the semantic information of the category network, and it improve the accuracy of the results. Besides, we analyze and evaluate the algorithm by comparing the calculation results with famous knowledge base (e.g., Hownet) and traditional methods based on Wikipedia on the same test set, and prove its superiority.
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