title: Grounding co-occurrence: Identifying features in a lexical co-occurrence model of semantic memory.
abstract: Lexical co-occurrence models of semantic memory represent word meaning by vectors in a high-dimensional space. These vectors are derived from word usage, as found in a large corpus of written text. Typically, these models are fully automated, an advantage over models that represent semantics that are based on human judgments (e.g., feature-based models). A common criticism of co-occurrence models is that the representations are not grounded: Concepts exist only relative to each other in the space produced by the model. It has been claimed that feature-based models offer an advantage in this regard. In this article, we take a step toward grounding a co-occurrence model. A feed-forward neural network is trained using back propagation to provide a mapping from co-occurrence vectors to feature norms collected from subjects. We show that this network is able to retrieve the features of a concept from its co-occurrence vector with high accuracy and is able to generalize this ability to produce an appropriate list of features from the co-occurrence vector of a novel concept.
authors.affiliation: University of Windsor, Windsor, Ontario, Canada.
authors.forename: Kevin
authors.initials: K
authors.lastname: Durda
authors.forename: Lori
authors.initials: L
authors.lastname: Buchanan
authors.forename: Richard
authors.initials: R
authors.lastname: Caron
mesh_terms: D000465:Algorithms; D056344:Executive Function / Q000502:physiology; D006801:Humans; D008568:Memory / Q000502:physiology*; D011939:Mental Recall; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D012660:Semantics