In this paper we focus on the automatic evaluation of argumentative essays, for scaffolding improvements in writing skills. Our goal is providing an automated approach to classify argumentative elements as "effective", "adequate", or "ineffective". We propose the usage of an additional feature, called ranking score, in the training process of a text-based classifier. The ranking score is obtained by performing argumentative reasoning on the different argumentative elements of an essay. We experimentally show that the introduction of this feature leads to improved performance of both Ada boost classifier and biLSTM neural network
Argumentation Ranking Semantics as a Feature for Classification - On Automatic Evaluation of Argumentative Essays
Claudia d'Amato;Nicola Di Mauro;Stefano Ferilli;Nunzia Lomonte
2022-01-01
Abstract
In this paper we focus on the automatic evaluation of argumentative essays, for scaffolding improvements in writing skills. Our goal is providing an automated approach to classify argumentative elements as "effective", "adequate", or "ineffective". We propose the usage of an additional feature, called ranking score, in the training process of a text-based classifier. The ranking score is obtained by performing argumentative reasoning on the different argumentative elements of an essay. We experimentally show that the introduction of this feature leads to improved performance of both Ada boost classifier and biLSTM neural networkFile in questo prodotto:
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