Using Information Content to Evaluate Semantic Similarity in a Taxonomy
<|reference_start|>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).<|reference_end|>
@article{resnik1995using,
    title={Using Information Content to Evaluate Semantic Similarity in a Taxonomy},
    author={Philip Resnik},
    journal={Proceedings of the 14th International Joint Conference on
  Artificial Intelligence},
    year={1995},
    archivePrefix={arXiv},
    eprint={cmp-lg/9511007},
    primaryClass={cmp-lg cs.CL}
}