Improving sparse word similarity models with asymmetric measures

Abstract

We show that asymmetric models based on Tversky (1977) improve correlations with human similarity judgments and nearest neighbor discovery for both frequent and middle-rank words. In accord with Tversky's discovery that asymmetric similarity judgments arise when comparing sparse and rich representations, improvement on our two tasks can be traced to heavily weighting the feature bias toward the rarer word when comparing high- and midfrequency words. © 2014 Association for Computational Linguistics.