Paper: Generalizing over Lexical Features: Selectional Preferences for Semantic Role Classification

ACL ID P09-2019
Title Generalizing over Lexical Features: Selectional Preferences for Semantic Role Classification
Venue Annual Meeting of the Association of Computational Linguistics
Session Short Paper
Year 2009
Authors

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