Exploiting full parsing information to label semantic roles using an ensemble of ME and SVM via integer linear programming
Richard Tzong‐Han Tsai, Chia‐Wei Wu, Yu‐Chun Lin, Wen‐Lian Hsu · 2005
In this paper, we propose a method that exploits full parsing information by representing it as features of argument classification models and as constraints in integer linear learning programs.In addition, to take advantage of SVM-based and Maximum Entropy-based argument classification models, we incorporate their scoring matrices, and use the combined matrix in the above-mentioned integer linear programs.The experimental results show that full parsing information not only increases the F-score of argument classification models by 0.7%, but also effectively removes all labeling inconsistencies, which increases the F-score by 0.64%.The ensemble of SVM and ME also boosts the F-score by 0.77%.Our system achieves an F-score of 76.53% in the development set and 76.38% in Test WSJ.