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https://hdl.handle.net/1822/79452
Título: | Applying machine learning classifiers in argumentation context |
Autor(es): | Conceição, Luís Carneiro, João Marreiros, Goreti Novais, Paulo |
Palavras-chave: | Argument mining Argumentation-based dialogues Machine learning classifiers |
Data: | 2021 |
Editora: | Springer, Cham |
Revista: | Advances in Intelligent Systems and Computing |
Citação: | Conceição, L., Carneiro, J., Marreiros, G., Novais, P. (2021). Applying Machine Learning Classifiers in Argumentation Context. In: Dong, Y., Herrera-Viedma, E., Matsui, K., Omatsu, S., González Briones, A., Rodríguez González, S. (eds) Distributed Computing and Artificial Intelligence, 17th International Conference. DCAI 2020. Advances in Intelligent Systems and Computing, vol 1237. Springer, Cham. https://doi.org/10.1007/978-3-030-53036-5_34 |
Resumo(s): | Group decision making is an area that has been studied over the years. Group Decision Support Systems emerged with the aim of supporting decision makers in group decision-making processes. In order to properly support decision-makers these days, it is essential that GDSS provide mechanisms to properly support decision-makers. The application of Machine Learning techniques in the context of argumentation has grown over the past few years. Arguing includes negotiating arguments for and against a certain point of view. From political debates to social media posts, ideas are discussed in the form of an exchange of arguments. During the last years, the automatic detection of this arguments has been studied and it’s called Argument Mining. Recent advances in this field of research have shown that it is possible to extract arguments from unstructured texts and classifying the relations between them. In this work, we used machine learning classifiers to automatically classify the direction (relation) between two arguments. |
Tipo: | Artigo em ata de conferência |
URI: | https://hdl.handle.net/1822/79452 |
ISBN: | 978-3-030-53035-8 |
e-ISBN: | 978-3-030-53036-5 |
DOI: | 10.1007/978-3-030-53036-5_34 |
ISSN: | 2194-5357 |
Versão da editora: | https://link.springer.com/chapter/10.1007/978-3-030-53036-5_34 |
Arbitragem científica: | yes |
Acesso: | Acesso restrito UMinho |
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Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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Paper DCAI 2020_vCameraReady.pdf Acesso restrito! | 144,71 kB | Adobe PDF | Ver/Abrir |