SpaceCase: A Model of Spatial Preposition Use

We present SpaceCase, a computational model of spatial preposition use that combines geometric and functional influences. SpaceCase treats spatial preposition use as governed by evidential rules, each representing influences of particular factors. Our model is unique in relying on both automatically constructed visual representations from sketched input, and drawing our functional representations from an independently derived large knowledge base, both of which reduce tailorability. SpaceCase can account for the results of Feist and Gentner (2003), whose experiments about in/on judgments in native English speakers showed the influence of four factors: (1) geometry of the ground, (2) animacy of the ground (3) animacy of the figure and (4) function of the ground. SpaceCase also captures Feist and Gentner’s (2001) result that memory for spatial relationships can be influenced by spatial language during encoding.