Semantic Similarity in a Taxonomy: An Information-Based Measure and its Application to Problems of Ambiguity in Natural Language
Journal of Artificial Intelligence ResearchPublished 1 July 1999Open access
Philip Resnik
Citations2,085
SJR quartileQ1
SJR score1.37
SNIP2.97
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TL;DR
This article presents a measure of semantic similarity in an IS-A taxonomy based on the notion of shared information content that performs better than the traditional edge-counting approach.
Abstract
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.
Keywords
Computer Science
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