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A computational model for spatial expression resolution

This paper presents a computational model for the interpretation of linguistic spatial propositions in the restricted realm of a 2D puzzle game. Based on an experiment aimed at analyzing human judgment of spatial expressions, we establish a set of criteria that explain human preference for certain interpretations over others. For each of these criteria, we define a metric that combines the semantic and pragmatic contextual information regarding the game as well as the utterance being resolved. Each metric gives rise to a potential field that characterizes the degree of likelihood for carrying out the instruction at a specific hypothesized location. We resort to machine learning techniques to determine a model of spatial relationships from the data collected during the experiment. Sentence interpretation occurs by matching the potential field of each of its possible interpretations to the model at hand. The system's explanation capabilities lead to the correct assessment of ambiguous situated utterances for a large percentage of the collected expressions.