Abstract
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.
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Notes
- 1.
The MMAX2 software is platform-independent, written in java and can freely be downloaded from http://mmax2.sourceforge.net/.
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Acknowledgements
This work was partially supported by national funds through FCT – Fundação para a Ciência e Tecnologia, under project Pest-OE/EEI/LA0021/2013 and project PEst-OE/LIN/UI0214/2013.
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Sequeira, J., Gonçalves, T., Quaresma, P., Mendes, A., Hendrickx, I. (2018). Using Syntactic and Semantic Features for Classifying Modal Values in the Portuguese Language. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2016. Lecture Notes in Computer Science(), vol 9624. Springer, Cham. https://doi.org/10.1007/978-3-319-75487-1_28
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