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Open AccessArticle

Automatic Classification of Text Complexity

1
Department of Humanities and Social Sciences, University for Foreigners of Perugia, 06123 Perugia, Italy
2
Istituto per Applicazioni del Calcolo, CNR, 00185 Roma, Italy
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Appl. Sci. 2020, 10(20), 7285; https://doi.org/10.3390/app10207285
Received: 22 September 2020 / Revised: 12 October 2020 / Accepted: 13 October 2020 / Published: 18 October 2020
(This article belongs to the Special Issue Advances in Artificial Intelligence Learning Technologies)
This work introduces an automatic classification system for measuring the complexity level of a given Italian text under a linguistic point-of-view. The task of measuring the complexity of a text is cast to a supervised classification problem by exploiting a dataset of texts purposely produced by linguistic experts for second language teaching and assessment purposes. The commonly adopted Common European Framework of Reference for Languages (CEFR) levels were used as target classification classes, texts were elaborated by considering a large set of numeric linguistic features, and an experimental comparison among ten widely used machine learning models was conducted. The results show that the proposed approach is able to obtain a good prediction accuracy, while a further analysis was conducted in order to identify the categories of features that influenced the predictions. View Full-Text
Keywords: natural language processing; text classification; measuring text complexity natural language processing; text classification; measuring text complexity
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Santucci, V.; Santarelli, F.; Forti, L.; Spina, S. Automatic Classification of Text Complexity. Appl. Sci. 2020, 10, 7285.

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