Using network clustering to uncover the taxonomic and thematic structure of the mental lexicon

While still influential, the view that concepts are organized as a hierarchical taxonomy as proposed by Rosch (1973) has been challenged on several occasions. For example, some studies have attributed a larger role to thematic relations (Gentner and Kurtz, 2005; Lin and Murphy, 2001), whereas others have stressed the role of affect in structuring word meaning (Niedenthal et al., 1999). A comprehensive account of how these different principles shape and structure meaning in the lexicon is missing, and most studies continue to be biased towards concrete noun categories that fit into hierarchical taxonomies (Medin and Rips, 2005). To capture mental or psychological properties that organize the lexicon for a wide range of concepts and semantic relations, we propose a large-scale semantic network derived from word associations as the basis to uncover what the structural principles are.