A Two-Phase Approach Towards Identifying Argument Structure in Natural Language
Table 1: Classifier Performance: mean values are reported for Support (S) and Neutral (N) relations.
['[BOLD] Measure', '[BOLD] type-1 SVM', '[BOLD] type-2 SVM', '[BOLD] type-2 MLP']
We have shown 7 performance measures for each classifier. Specifically, we present the mean of the confidence values provided by the classifier for each class label, which is used directly in the Structure Prediction phase. A mean confidence of 1, therefore, will be the perfect score for Support pairs. Similarly, a mean confidence of 0 will be the perfect score for Neutral pairs. We can observe that each classifier outperforms the others for at least some metric. One can observe that type-2 classifiers perform better in predicting Neutral pairs. The confidence measure is lower than type-1 and the recall is higher as well. However, the precision for Neutral is better for type-1 SVM because of the data imbalance. Type-2 MLP gave the best accuracy in our experiments.