Semantic Representation and Priming in a Self-organizing Lexicon

This paper presents a model of the mental lexicon and its formation, based on the self-organizing neural network. When exposed to raw text, the model clusters words according to their semantic relatedness to form a semantic network 7]. Simulations using artiicial data are described that show how co-occurrence information can be used to create a low-dimensional representation of lexical semantics. The model also suggests a novel explanation of semantic priming based on topographic organization and co-occurrence statistics. A simulation that implements this explanation is presented, and the model is tested against priming eeects found in Moss et al.'s 25] semantic priming experiment.