Paper: Exploiting Full Parsing Information To Label Semantic Roles Using An Ensemble Of ME And SVM Via Integer Linear Programming

ACL ID W05-0638
Title Exploiting Full Parsing Information To Label Semantic Roles Using An Ensemble Of ME And SVM Via Integer Linear Programming
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2005
Authors

In this paper, we propose a method that exploits full parsing information by repre- senting it as features of argument classifi- cation models and as constraints in integer linear learning programs. In addition, to take advantage of SVM-based and Maxi- mum Entropy-based argument classifica- tion models, we incorporate their scoring matrices, and use the combined matrix in the above-mentioned integer linear pro- grams. The experimental results show that full parsing information not only in- creases the F-score of argument classifi- cation models by 0.7%, but also effectively removes all labeling inconsis- tencies, 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 Te...