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.
|Number of pages||4|
|State||Published - 2005|
|Event||9th Conference on Computational Natural Language Learning, CoNLL 2005 - Ann Arbor, MI, United States|
Duration: 29 Jun 2005 → 30 Jun 2005
|Conference||9th Conference on Computational Natural Language Learning, CoNLL 2005|
|City||Ann Arbor, MI|
|Period||29/06/05 → 30/06/05|