Paper: Semantic Role Labeling Of NomBank: A Maximum Entropy Approach

ACL ID W06-1617
Title Semantic Role Labeling Of NomBank: A Maximum Entropy Approach
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2006
Authors

This paper describes our attempt at NomBank-based automatic Semantic Role Labeling (SRL). NomBank is a project at New York University to annotate the ar- gument structures for common nouns in the Penn Treebank II corpus. We treat the NomBank SRL task as a classifica- tion problem and explore the possibility of adapting features previously shown use- ful in PropBank-based SRL systems. Var- ious NomBank-specific features are ex- plored. On test section 23, our best sys- tem achieves F1 score of 72.73 (69.14) when correct (automatic) syntactic parse trees are used. To our knowledge, this is the first reported automatic NomBank SRL system.