Proceedings of the Annual Meeting of the Cognitive Science Society

Semantic space models based on distributional information and semantic network (graphical) models are two of the mostpopular models of semantic representation. Both types of models succeed at modeling or explaining various tasks. Bothtypes of models also have limitations. Spatial models have difficulties representing indirect semantic relations, whilegraphical models have lacked a theoretical account for the construction of their semantic network. In this article, wedevelop the Distributional Graph Model. The new model resembles semantic space models in the way that it is a repre-sentation of semantic memory obtained from statistical learning on a linguistic corpus. But like other graphical models,it is able to capture indirect semantic relatedness as well. Using an artificial language specifically designed to test differ-ent types of syntagmatic and paradigmatic relationships, we show that the Distributional Graph Model demonstrates thebenefits of both graphical and spatial distributional models.