In two studies we compare a distributional semantic model derived from word co-occurrences and a word association based model in their ability to predict properties that affect lexical processing. We focus on age of acquisition, concreteness, and three affective variables, namely valence, arousal, and dominance, since all these variables have been shown to be fundamental in word meaning. In both studies we use a model based on data obtained in a continued free word association task to predict these variables. In Study 1 we directly compare this model to a word co-occurrence model based on syntactic dependency relations to see which model is better at predicting the variables under scrutiny in Dutch. In Study 2 we replicate our findings in English and compare our results to those reported in the literature. In both studies we find the word association-based model fit to predict diverse word properties. Especially in the case of predicting affective word properties, we show that the association model is superior to the distributional model.

Additional Metadata
Keywords word associations, k-nearest neighbors, lexical norms, affective word characteristics, concreteness, age of acquisition
Persistent URL dx.doi.org/10.5334/joc.50, hdl.handle.net/1765/125413
Journal Journal of Cognition
Citation
Vankrunkelsven, H, Verheyen, S, Storms, G, & De Deyne, S. (2018). Predicting lexical norms: A comparison between a word association model and text-based word co-occurrence models. Journal of Cognition, 1(1). doi:10.5334/joc.50