A multi- versus a single-classifier approach for the identification of modality in the portuguese language

Abstract

This work presents a comparative study between two different approaches to build an automatic classification system for Modality values in the Portuguese language. One approach uses a single multi-class classifier with the full dataset that includes eleven modal verbs; the other builds different classifiers, one for each verb. The performance is measured using precision, recall and F 1 . Due to the unbalanced nature of the dataset a weighted average approach was calculated for each metric. We use support vector machines as our classifier and experimented with various SVM kernels to find the optimal classifier for the task at hand. We experimented with several different types of feature attributes representing parse tree information and compare these complex feature representation against a simple bag-of-words feature representation as baseline. The best obtained F 1 values are above 0.60 and from the results it is possible to conclude that there is no significant difference between both approaches.