Using Information Content to Evaluate Semantic Similarity in a Taxonomy

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dc.creatorResnik, Philip
dc.date1995-11-29
dc.date.accessioned2026-07-07T09:10:04Z
dc.date.available2026-07-07T09:10:04Z
dc.descriptionThis paper presents a new measure of semantic similarity in an IS-A taxonomy, based on the notion of information content. Experimental evaluation suggests that the measure performs encouragingly well (a correlation of r = 0.79 with a benchmark set of human similarity judgments, with an upper bound of r = 0.90 for human subjects performing the same task), and significantly better than the traditional edge counting approach (r = 0.66).
dc.description6 pages, 2 postscript figures, uses ijcai95.sty
dc.identifierhttps://arxiv.org/abs/cmp-lg/9511007
dc.identifierhttp://arxiv.org/abs/cmp-lg/9511007
dc.identifierProceedings of the 14th International Joint Conference on Artificial Intelligence
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/151250
dc.subjectComputation and Language
dc.titleUsing Information Content to Evaluate Semantic Similarity in a Taxonomy
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