Paper: Improving Semantic Role Classification with Selectional Preferences

ACL ID N10-1058
Title Improving Semantic Role Classification with Selectional Preferences
Venue Human Language Technologies
Session Main Conference
Year 2010
Authors

This work incorporates Selectional Prefer- ences (SP) into a Semantic Role (SR) Clas- sification system. We learn separate selec- tional preferences for noun phrases and prepo- sitional phrases and we integrate them in a state-of-the-art SR classification system both in the form of features and individual class predictors. We show that the inclusion of the refined SPs yields statistically significant im- provements on both in domain and out of do- main data (14.07% and 11.67% error reduc- tion, respectively). The key factor for success is the combination of several SP methods with the original classification model using meta- classification.