Utilize este identificador para referenciar este registo: http://hdl.handle.net/10451/30783
Título: Using syntactic and semantic features for classifying modal values in the Portuguese language
Autor: Sequeira, João
Gonçalves, Teresa
Quaresma, Paulo
Mendes, Amália
Hendrickx, Iris
Data: 2016
Editora: Springer
Citação: Sequeira, João, Teresa Gonçalves, Paulo Quaresma, Amália Mendes & Iris Hendrickx (2016) Using syntactic and semantic features for classifying modal values in the Portuguese language. In: Proceedings of CICLing-16, 17th international Conference on Intelligent Text Processing and Computational Linguistics, Lecture Notes in Computer Science. Springer.
Resumo: This paper presents a study made in a eld poorly explored in the Portuguese language { modality and its automatic tagging. Our main goal was to nd 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 F1. Because it is a relatively unexplored eld, 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 di erent 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 F1 when compared to the traditional bow approach.
URI: http://hdl.handle.net/10451/30783
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