VECTOR BASED SEMANTIC ANALYSIS REVEALS ABSENCE OF COMPETITION AMONG RELATED SENSES

Abstract

Previous research demonstrated that processing time was facilitated bynumber of related word senses (polysemy) and inhibited by number of unrelatedword meanings (homonymy). The starting point of this research were thefindings described by Moscoso del Prado Martín and colleagues, who offereda unique account of processing of two forms of lexical ambiguity. By applyingthe techniques they proposed, for the set of strictly polysemous Serbian nounswe calculated ambiguity measures they introduced. Based on the covariancematrix of the context vectors, we derived entropy of equivalent Gaussiandistribution, and based on the context vectors probability density function,we derived differential entropy. Negentropy was calculated as the differencebetween the two. Based on interpretation that entropy of equivalent Gaussianmirrors sense cooperation, or polysemy, while negentropy mirrors meaningcompetition, or homonymy, we predicted that in the set of strictly polysemousnouns, negentropy effect would disappear. In accordance with our predictions,entropy of equivalent Gaussian distribution accounted for significant proportionof processing latencies variance. Negentropy did not affect reaction time. Thisfinding is in accordance with the hypothesis that entropy of equivalent Gaussiandistribution, as a measure of general width of activation in semantic space, reflects polysemy, that is, the existence of related senses. Therefore, polysemyadvantage could be the result of the wide-spread activation in semantic spaceand reduced competition among overlapping Gaussians.

Keywords

• polysemy
• semantic space
• context vectors
• differential entropy
• equivalent Gaussian entropy
• negentropy