This paper presents a new resource for automatically assessing text difﬁculty in the context of Italian as a second or foreign language learning and teaching. It is called MALT-IT2, and it automatically classiﬁes inputted texts according to the CEFR level they are more likely to belong to. After an introduction to the ﬁeld of automatic text difﬁculty assessment, and an overview of previous related work, we describe the rationale of the project, the corpus and computational system it is based on. Experiments were conducted in order to investigate the reliability of the system. The results show that the system is able to obtain a good prediction accuracy, while a further analysis was conducted in order to identify the categories of features which mostly inﬂuenced the predictions.
|Titolo:||MALT-IT2: A New Resource to Measure Text Difficulty in light of CEFR levels for Italian L2 learning|
|Data di pubblicazione:||2020|
|Appare nelle tipologie:||4.1 Contributo in Atti di convegno|