Paper: Experimental Evaluation of LTAG-Based Features for Semantic Role Labeling

ACL ID D07-1062
Title Experimental Evaluation of LTAG-Based Features for Semantic Role Labeling
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2007
Authors

This paper proposes the use of Lexical- ized Tree-Adjoining Grammar (LTAG) for- malism as an important additional source of features for the Semantic Role Labeling (SRL) task. Using a set of one-vs-all Sup- port Vector Machines (SVMs), we evalu- ate these LTAG-based features. Our exper- iments show that LTAG-based features can improve SRL accuracy significantly.