A Comparative Study of Different Classification Methods for the Identification of Brazilian Portuguese Multiword Expressions

This paper presents a comparative study of different methods for the identification of multiword expressions, applied to a Brazilian Portuguese corpus. First, we selected the candidates based on the frequency of bigrams. Second, we used the linguistic information based on the grammatical classes of the words forming the bigrams, together with the frequency information in order to compare the performance of different classification algorithms. The focus of this study is related to different classification techniques such as support-vector machines (SVM), multi-layer perceptron, naïve Bayesian nets, decision trees and random forest. Third, we evaluated three different multi-layer perceptron training functions in the task of classifying different patterns of multiword expressions. Finally, our study compared two different tools, MWEtoolkit and Text-NSP, for the extraction of multiword expression candidates using different association measures.