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A Novel WordNet-based Approach for Measuring Semantic Similarity

机译：一种新的基于WordNet的语义相似度度量方法

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摘要

WordNet is one of the most widely used online semantic dictionaries. In this paper, we propose a new word semantic similarity approach based on the path and depth in WordNet. This approach exploits the shortest path between two words and the depth of their lowest common hypernym in the hierarchy tree to measure the semantic similarity between two words. Considering the differences of directly hyponym number of the lowest common hypernym between different word pairs, we adopt the directly hyponym number of lowest common hypernym to adjust the shortest path between two words and balance their similarity calculation. The experiments show that, the Pearson correlation coefficient, between the human judgments in MC30 dataset and the computational measures presented in this study is 0.8597, which is better than most of the current related algorithms.
机译：WordNet是使用最广泛的在线语义词典之一。本文基于WordNet的路径和深度，提出了一种新的词语义相似度方法。该方法利用两个单词之间的最短路径以及层次结构树中它们的最低共同上位字母的深度来测量两个单词之间的语义相似性。考虑到不同词对之间最低共同上位词的直接下位数的差异，我们采用最低共同上位词的直接下位数来调整两个词之间的最短路径并平衡其相似度计算。实验表明，本研究提出的MC30数据集的人为判断与计算量之间的皮尔逊相关系数为0.8597，优于目前大多数相关算法。

著录项

  • 来源
    《Journal of information and computational science》 |2015年第13期|4919-4927|共9页
  • 作者

    Xinhua Zhu; Fei Li; Hongchao Chen; Junqing Mao;

  • 作者单位

    Guangxi Key Lab of Multi-source Information Mining & Security, Guangxi Normal University Guilin 541004, China;

    Guangxi Key Lab of Multi-source Information Mining & Security, Guangxi Normal University Guilin 541004, China;

    Guangxi Key Lab of Multi-source Information Mining & Security, Guangxi Normal University Guilin 541004, China;

    Guangxi Key Lab of Multi-source Information Mining & Security, Guangxi Normal University Guilin 541004, China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    WordNet; Semantic Similarity; Path; Depth; Lowest Common Hypernym;

    机译：词网;语义相似度;路径;深度;最低常见上位词;

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