Abstract
Table 3.1: Distribution of the classes in the corpus after the simplifying restriction.
['[BOLD] NSU class', '[BOLD] Total']
For the classification task, we make the same simplifying restriction on the corpus made by \citetFernandez:2007, that is to consider only the NSUs whose antecedent is their preceding sentence. This assumption facilitates the feature extraction procedure without reducing significantly the size of the dataset (about 12% of the total). Moreover very frequent classes are usually the easiest to classify, leaving the most difficult ones with few instances as examples for the classifiers. Although the scarcity of the training material and the imbalance of the classes are the two major problems for the classification task, we propose a set of methods to address them, as described in the following sections. The scarcity of labeled data is probably the major problem to face in this classification task. However, a large amount of unlabeled data is available in the BNC. There are many classification tasks, such as ours, in which it is hard or costly to label a large amount of instances while instead it is relatively cheap to extract unlabeled ones. The empirical question is whether the use of unlabeled data is useful to improve the classification performances. Semi-Supervised Learning techniques deal with this issue. They exploit the combination of a small amount of labeled data and a large amount of unlabeled data to try improve the classification accuracy. Even though it is still a young research field, semi-supervised learning has already found many fields of application \citepliang2005semi,bergsma2010large.