Abstract
The task of classification with multi-label data is an important research field in Natural Language Processing (NLP). While there have been studies using one-stage multi-label approaches for automatic text classification, there are not many that use two-stages stacking models. In this paper we explored Binary Relevance (BR) classifiers, with J48 and probabilistic Support Vector Machine (SVM), in a two-stage stacking model. We have evaluated our proposal in three textual data sets: The Movie Database (TMDB), Enron email, and EURLEX European legal text. The results showed that by using a two-stage stacking model, we can obtain better results than by using one-stage classifiers.
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This study was financed in part by the Conselho Nacional de Desenvolvimento Científico e Tecnológico - Brasil (CNPq).
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Nunes, R.M., Domingues, M.A., Feltrim, V.D. (2019). Exploring Multi-label Stacking in Natural Language Processing. In: Moura Oliveira, P., Novais, P., Reis, L. (eds) Progress in Artificial Intelligence. EPIA 2019. Lecture Notes in Computer Science(), vol 11805. Springer, Cham. https://doi.org/10.1007/978-3-030-30244-3_58
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DOI: https://doi.org/10.1007/978-3-030-30244-3_58
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