Abstract
Table 3.5: Distribution of the classes of the instances annotated with Active Learning.
['[BOLD] NSU Class', '[BOLD] Instances']
Active Learning Our active learning experiment was carried out using the JCLAL library. For the active learning process we divided the dataset into three parts: training set (50%), development set (25%) and test set (25%). At each iteration, the JCLAL library builds a classifier on the training set and evaluates it over the development set. The user is then asked to annotate the selected instance. The process iterates in this manner until the stopping criteria is met, that is when the goal of 100 newly annotated instances is reached. This process has been performed once with the extended feature set and the SMO classifier. Secondly, it has been simulated (i.e. using the data obtained in the previous run) using the baseline feature set instead. All the performance measures are clearly improving as new instances become available, for both the extended feature set and the baseline one.