A Simple, Similarity-based Model for Selectional Preferences
Katrin Erk · 2007
We propose a new, simple model for the auto-matic induction of selectional preferences, using corpus-based semantic similarity metrics. Fo-cusing on the task of semantic role labeling, we compute selectional preferences for seman-tic roles. In evaluations the similarity-based model shows lower error rates than both Resnik’s WordNet-based model and the EM-based clus-tering model, but has coverage problems.