Publicação

A Multi- versus a Single-classifier Approach for the Identification of Modality in the Portuguese Language.

Detalhes bibliográficos
Resumo:	This work presents a comparative study between two different approaches to build an automatic classification system for Modalityvalues 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 F1. Due to the unbalanced nature of the dataset a weighted average approach was calculated for each metric. We use support vector machines as ourclassifier 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 F1values are above 0.60 and from the results it is possible to conclude that there is no significant difference between both approaches.
Autores principais:	Sequeira, João
Outros Autores:	Gonçalves, Teresa; Quaresma, Paulo; Mendes, Amália; Hendrickx, Iris
Assunto:	Natural language processing Modality Feature selection Support Vector Machines
Ano:	2018
País:	Portugal
Tipo de documento:	artigo
Tipo de acesso:	acesso aberto
Instituição associada:	Universidade de Lisboa
Idioma:	inglês
Origem:	Repositório da Universidade de Lisboa

Descrição
Resumo:	This work presents a comparative study between two different approaches to build an automatic classification system for Modalityvalues 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 F1. Due to the unbalanced nature of the dataset a weighted average approach was calculated for each metric. We use support vector machines as ourclassifier 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 F1values are above 0.60 and from the results it is possible to conclude that there is no significant difference between both approaches.