Semantic similarity in a taxonomy: An information-based measure and its application to problems of ambiguity in natural language
Semantic similarity in a taxonomy: An information-based measure and its application to problems of ambiguity in natural language
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DOI：
10.1613/jair.514
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发表时间：
1999-01-01
影响因子：
5
通讯作者：
Resnik, P
中科院分区：
文献类型：
--
作者：
Resnik, P
本文提出了一种基于共享信息内容概念的is分类法中的语义相似性度量方法。对人类相似性判断的基准集的实验评估表明，该措施比传统的边缘计数方法表现得更好。本文提出了利用分类相似性解决句法和语义歧义的算法，沿着实验结果证明了它们的有效性。
This article presents a measure of semantic similarity in an is-a taxonomy based on the notion of shared information content. Experimental evaluation against a benchmark set of human similarity judgments demonstrates that the measure performs better than the traditional edge-counting approach. The article presents algorithms that take advantage of taxonomic similarity in resolving syntactic and semantic ambiguity, along with experimental results demonstrating their effectiveness.