Using Information Content to Evaluate Semantic Similarity in a Taxonomy

This 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).

• Publication year

  1995

• Venue

  International Joint Conference on Artificial Intelligence

• Publication date

  1995-08-20

• Fields of study

  Computer Science

• Identifiers
  arXiv cmp-lg/9511007
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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