Fusing distributional and experiential information for measuring semantic relatedness

Models of semantic relatedness have usually focused on language-based distributional information without taking into account ''experiential data'' concerning the embodied sensorial source of the represented concepts. In this paper, we present an integrative cognitive model of semantic relatedness. The model - semantic family resemblance - uses a variation of the co-product as a mathematical structure that guides the fusion of distributional and experiential information. Our algorithm provides superior results in a set expansion task and a significant correlation with two benchmarks of human rated word-pair similarity datasets.