Experimental Evaluation of LTAG-Based Features for Semantic Role Labeling

In this technical report, we propose the use of Lexicalized Tree-Adjoining Grammar (LTAG) formalism as an important additional source of features for the Semantic Role Labeling (SRL) task. Using a set of one-vs-all Support Vector Machines (SVMs), we evaluate these LTAG-based features. Our experiments show that LTAG-based features can improve SRL accuracy significantly. When compared with the best known set of features that are used in state of the art SRL systems we obtain an improvement in F-score from 82.34% to 85.25%.

• Type: Conference
• Year: 2007
• Where: EMNLP
• Authors: Yudong Liu, Anoop Sarkar
• Keywords: EMNLP 2007, Important Additional Source, LTAG-based Features, Natural Language Processing, Semantic Role Labeling