Generalizing over Lexical Features: Selectional Preferences for Semantic Role Classification

This paper explores methods to alleviate the effect of lexical sparseness in the classification of verbal arguments. We show how automatically generated selectional preferences are able to generalize and perform better than lexical features in a large dataset for semantic role classification. The best results are obtained with a novel second-order distributional similarity measure, and the positive effect is specially relevant for out-of-domain data. Our findings suggest that selectional preferences have potential for improving a full system for Semantic Role Labeling.

• Type: Journal
• Year: 2009
• Where: ACL
• Authors: Beñat Zapirain, Eneko Agirre, Lluís Màrquez
• Keywords: ACL 2009, Computational Linguistics, Selectional Preferences, Semantic Role, Semantic Role Classification