## 2 Background  
There is a wide range of classification algorithms applied to NLP, some of the most popular ones are [13; 14; 15]:  
* Decision tree-based approaches, which select tags for input values devising decision rules; two of the most popular ones are
* CART (Classification and Regression Tree), which partitions the continuous attribute value into a discrete set of intervals. CART, unlike other decision-tree classifiers, does not compute rule sets but supports numerical target variables (regression). CART constructs binary trees using the feature and threshold that yield the most significant information gain at each node [16]; and,
* Random Forest, which improves the classification capacity and controls over-fitting by assembling several decision trees built on different sub-samples of the dataset [17]. * Bayesian classifiers, which estimate how each attribute value impacts on the likelihood of the class to be assigned. The Naive Bayes method is the most simplified Bayesian classifier; it applies the Bayes theorem with the 'naive' assumption of conditional independence between every pair of features. It is well known that Naive Bayes classifiers have worked markedly well in real-world situations, even when the underlying independence assumption is violated [18]. * Proximity-based classifiers, which use distance measures to classify under the premise that texts belonging to the same category are 'closer' than those in other classes. Perhaps, the \(k\)NN (\(k\)-nearest neighbors) method is the archetype of this kind of lazy learners. \(k\)NN predicts the label of a pattern by searching the \(k\) training samples that are the closest to the new entry. * Linear classifiers, which classify based on the value of the linear combinations of the document features, trying to find 'good' linear separators among classes. Support Vector Machine (SVM) falls into this category. For a given training dataset, this eager classifier searches for an optimum hyperplane that classifies new samples [14]. In the scientific literature, they are popular text classifiers (e.g., [19; 20; 21; 22; 23; 24]), because they are less sensitive (than other classifiers) to the presence of some potential issues of text mining, namely high-dimensional feature space, sparse vectors, and irrelevant features (cf. [19; 25]). Unstructured texts must be represented according to a vector space model to build an efficient classification system. In this regard, the BoW is one of the basic approaches, which forms a vector to characterize a text by counting the frequency of each term. Most text classification approaches utilize a BoW representation, because it is easy to adapt, extend, apply, and enrich. Unfortunately, BoW-based schemata also have some limitations:1. high dimensionality of the vector representation,
2. loss of correlation with adjacent words, and
3. loss of semantic relationships among terms. Researchers have attempted to address the above issues by incorporating contextual and grammatical knowledge, and weights instead of term frequencies (e.g., [26; 27; 28; 29]). Besides, an accessible and straightforward way to mitigate the consequences of the mentioned issue (a) is feature selection (cf. [30]). Our proposal selects features through a filter based on Mutual Information (MI). MI-based filters measure the statistical independence between variables, including nonlinear relationships, which are invariant under transformations in the feature space. [PERSON] and [PERSON] [31] present an exhaustive study on feature selection methods that are based on MI. One difficulty for supervised learning methods is the presence of class imbalance, in which the number of observations for each class tag is severely disproportionate.