Title

A New Set Of Norms For Semantic Relatedness Measures

Abstract

We have elicited human quantitative judgments of semantic relatedness for 122 pairs of nouns and compiled them into a new set of relatedness norms that we call Rel-122. Judgments from individual subjects in our study exhibit high average correlation to the resulting relatedness means (r = 0.77, σ = 0.09, N = 73), although not as high as Resnik's (1995) upper bound for expected average human correlation to similarity means (r = 0.90). This suggests that human perceptions of relatedness are less strictly constrained than perceptions of similarity and establishes a clearer expectation for what constitutes human-like performance by a computational measure of semantic relatedness. We compare the results of several WordNet-based similarity and relatedness measures to our Rel-122 norms and demonstrate the limitations of WordNet for discovering general indications of semantic relatedness. We also offer a critique of the field's reliance upon similarity norms to evaluate relatedness measures. © 2013 Association for Computational Linguistics.

Publication Date

1-1-2013

Publication Title

ACL 2013 - 51st Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference

Volume

2

Number of Pages

890-895

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

Socpus ID

84906926363 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84906926363

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