Abstract
Table 3.10: Per class performances comparison between the baseline (J48, baseline feature set) and the final approach (SMO, extended feature set, AL instances).
['[BOLD] NSU Class', '[BOLD] Baseline  [BOLD] Precision', '[BOLD] Baseline  [BOLD] Recall', '[BOLD] Baseline  [ITALIC] F1 [BOLD] -score', '[BOLD] Final approach  [BOLD] Precision', '[BOLD] Final approach  [BOLD] Recall', '[BOLD] Final approach  [ITALIC] F1 [BOLD] -score']
The SVM algorithm does not perform particularly well with the baseline feature set but scales better than the J48 classifier after the inclusion of the additional features. Overall, the results demonstrate that the classification can be improved using a modest amount of additional training data combined with an extended feature set. For instance, Helpful Rejections are still the most difficult classes to classify, even with the addition of 21 new instances. One of the problems with Helpful Rejections is that they are connected to their antecedents mainly at the semantic level. Consider the following example of Helpful Rejection that is hard to classify: [label=(3.0), ref=0, labelsep=20pt, leftmargin=55pt, ref=(3.0)] {dialogue}\speak A There was one which you said Ernest Morris was born in 1950. \speakB Fifteen. [BNC: J9A 372–373]