A Computational Model to Disentangle Semantic Information Embedded in Word Association Norms

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Executive Summary

Understanding the structure of semantic knowledge is an open challenge of fundamental importance in cognitive science. Along the most powerful computational probabilistic approaches to this challenge, recent studies have used also the perspective offered by the theory of complex networks to gain insight on it. The main idea behind the network approach is to map empirical data onto a graph (usually called complex network) that summarizes the observed relations between words in a given experiment.

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