Paper: A Simple Similarity-based Model for Selectional Preferences

ACL ID P07-1028
Title A Simple Similarity-based Model for Selectional Preferences
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2007
Authors
  • Katrin Erk (University of Texas at Austin, Austin TX)

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.