Paper: Improving sparse word similarity models with asymmetric measures

ACL ID P14-2049
Title Improving sparse word similarity models with asymmetric measures
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

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 Tver- sky?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 mid- frequency words.