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Learning to Classify Text Complexity for the Italian Language Using Support Vector Machines
Santucci, Valentino
;
Forti, Luciana
;
Santarelli, Filippo
;
Spina, Stefania
;
Milani, Alfredo
In: Computational Science and Its Applications – ICCSA 2020 (2020)
Abstract:
Natural language processing is undoubtedly one of the most active fields of research in the machine learning community. In this work we propose a supervised classification system that, given in input a text written in the Italian language, predicts its linguistic complexity in terms of a level of the Common European Framework of Reference for Languages (better known as CEFR). The system was built by considering: (i) a dataset of texts labeled by linguistic experts was collected, (ii) some vectorisation procedures which transform any text to a numerical representation, and (iii) the training of a support vector machine’s model. Experiments were conducted following a statistically sound design and the experimental results show that the system is able to reach a good prediction accuracy.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7978617/
https://doi.org/10.1007/978-3-030-58802-1_27
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