Using Syntactic and Semantic Features for Classifying Modal Values in the Portuguese Language

This paper presents a study made in a field poorly explored in the Portuguese language – modality and its automatic tagging. Our main goal was to find a set of attributes for the creation of automatic taggers with improved performance over the bag-of-words (bow) approach. The performance was measured using precision, recall and \(F_1\). Because it is a relatively unexplored field, the study covers the creation of the corpus (composed by eleven verbs), the use of a parser to extract syntactic and semantic information from the sentences and a machine learning approach to identify modality values. Based on three different sets of attributes – from trigger itself and the trigger’s path (from the parse tree) and context – the system creates a tagger for each verb achieving (in almost every verb) an improvement in \(F_1\) when compared to the traditional bow approach.