发布时间：

2026-06-30

DOI码：

10.1007/s40747-024-01496-y

所属单位：

城市与环境科学学院

论文名称：

A hybrid model to improve IC-related metrics of semantic similarity between words

发表刊物：

Complex & Intelligent Systems

关键字：

Information content;Ontology;Semantic similarity;Wilcoxon Signed-Rank Test;WordNet

摘要：

This paper proposes a hybrid model to improve Information Content (IC) related metrics of semantic similarity between words, named IC SP, based on the essential hypothesis that IC and the shortest path are two relatively independent semantic evidences and have approximately equal influences to the semantic similarity metric. The paradigm of IC SP is to linearly combine the IC-related metric and the shortest path. Meanwhile, a transformation from the semantic similarity of the concepts to that of the words is presented by maximizing every component of IC SP. 13 improved IC-related metrics based on IC SP are formed and implemented on the experimental platform HESML Lastra-D & iacute;az (Inf Syst 66:97-118, 2017). Pearson's and Spearman's correlation coefficients on well-accepted benchmarks for the improved metrics compare to those for the original ones to evaluate IC SP. I introduce the Wilcoxon Signed-Rank Test needing no standard distribution hypothesis, while, this hypothesis is required by T-Test on the sample of small size. T-Test, as well as the Wilcoxon Signed-Rank Test, conduct on the differences of the correlative coefficients for improved and original metrics. It is expected that the improved IC-related metrics could significantly outperform their corresponding original ones, and the experimental results, including the comparisons of mean and maximum of correlation coefficients as well as the p-value and confidence interval of both tests, accomplish the anticipation in the vast majority of cases.

第一作者：

肖佳

论文类型：

期刊论文

论文编号：

1829523953010479104

文献类型：

00001

页面范围：

39

ISSN号：

2199-4536

是否译文：

否

发表时间：

2024-06-07