## 8 Conclusions and Future Work  
In this work we have introduced a computational system for automatically classifying Italian written texts according to their difficulty. A web-based interface is made publicly available at this URL: [[https://lol.unistrapg.it/malt](https://lol.unistrapg.it/malt)]([https://lol.unistrapg.it/malt](https://lol.unistrapg.it/malt)).  
CEFR levels were considered as target classes of the classification system, thus allowing us to reuse the teaching and assessment materials adopted for second language learning purposes in order to train a prediction model. A wide set of quantitative linguistic features were considered and computational procedures were devised in order to extract numeric features from a given text. Therefore, the linguistic features allowed us to vectorize the texts, so that the most common machine learning models can be used.  
Experiments were held in order to analyze the effectiveness and the reliability of the proposed classification system. In particular, ten different classification models from the machine learning literature were considered. Overall, the experimental results indicate that the proposed approach reaches a very good accuracy level, in particular when the random forest model is considered.  
Furthermore, indications regarding which features are more important in the classification process are provided. In particular, this latter point paves the way for an interesting direction for future works aiming at objectively modeling the linguistic aspects that make a text difficult or easy to understand. Another interesting point in this direction is also a possible contrastive and quantitative analysis in the features space of corpora belonging to different languages.  
Figure 5: 2D visualizations of the dataset obtained with two executions of t-SNE.  
Finally, from a computational point-of-view, interesting future lines of research regard: the use of semantic features [75; 76] for the task analyzed in this article, the automatic augmentation of a text dataset used, not for semantic, but syntactic and linguistic tasks, and the use of evolutionary search techniques (such as [77; 78; 79; 80]) for automatically tuning the classification engine without any manual intervention.  
Conceptualization, S.S., V.S., and L.F.; methodology, V.S.; software, F.S. and V.S.; validation, F.S., L.F., and S.S.; formal analysis, V.S., L.F., and F.S.; investigation, V.S., F.S., L.F., and S.S.; resources, L.F., V.S., and F.S.; data curation, F.S., L.F., and S.S.; writing--original draft preparation, V.S.; writing--review and editing, V.S.; visualization, V.S. and F.S.; supervision, S.S. and V.S.; project administration, S.S. and V.S.; funding acquisition, S.S. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by: (1) the grant 2018.0424.021 _MALT-IT2. 10 na visora computazionale per Misurare Automaticamente la Leggibilita dei Testi per studenti di Italiano L2_, co-funded by the University for Foreigners of Perugia and by the Fondazione Cassa di Risparmio di Perugia, (2) the research project _Universita per Stranieri di Perugia - Finanziamento per Progetti di Ricerca di Ateneo - PRA 2020_, and (3) the research project _Universita per Stranieri di Perugia - Artificial intelligence for education, social and human sciences_.  
The authors declare no conflict of interest.