Exploiting Image Generality for Lexical Entailment Detection

We exploit the visual properties of concepts for lexical entailment detection by examining a concept’s generality. We introduce three unsupervised methods for determining a concept’s generality, based on its related images, and obtain state-ofthe-art performance on two standard semantic evaluation datasets. We also introduce a novel task that combines hypernym detection and directionality, significantly outperforming a competitive frequencybased baseline.