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Using Information Content to Evaluate Semantic Similarity in a Taxonomy

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arxiv cmp-lg/9511007 v1 pith:UROD5SD5 submitted 1995-11-29 cmp-lg cs.CL

Philip Resnik This is my paper · ORCID
classification cmp-lgcs.CL
keywords similaritycontenthumaninformationmeasuresemantictaxonomyapproach
verification ladder T0 review T1 audit T2 compute T3 formal
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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).

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. When Should LLMs Be Less Specific? Selective Abstraction for Reliable Long-Form Text Generation

    cs.AI 2026-02 conditional novelty 6.0 of 10

    Atom-wise selective abstraction—replacing low-confidence factual claims with higher-confidence, less specific versions—improves the risk-coverage trade-off in long-form generation by up to 27.73% AURC over claim removal.

  2. Un cadre paraconsistant pour l'{\'e}valuation de similarit{\'e} dans les bases de connaissances

    cs.DB 2025-09 reject novelty 4.0 of 10

    The paper defines a similarity score S* that subtracts a contradiction ratio from a shared-property ratio, and organizes knowledge entities into threshold-based paraconsistent super-categories.

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