Accéder directement au contenu Accéder directement à la navigation
Communication dans un congrès

Reflecting Comprehension through French Textual Complexity Factors

Abstract : Research efforts in terms of automatic textual complexity analysis are mainly focused on English vocabulary and few adaptations exist for other languages. Starting from a solid base in terms of discourse analysis and existing textual complexity assessment model for English, we introduce a French model trained on 200 documents extracted from school manuals pre-classified into five complexity classes. The underlying textual complexity metrics include surface, syntactic, morphological, semantic and discourse specific factors that are afterwards combined through the use of Support Vector Machines. In the end, each factor is correlated to pupil comprehension metrics scores, spanning throughout multiple classes, therefore creating a clearer perspective in terms of measurements impacting the perceived difficulty of a given text. In addition to purely quantitative surface factors, specific parts of speech and cohesion have proven to be reliable predictors of learners' comprehension level, creating nevertheless a strong background for building dependable French textual complexity models.
Type de document :
Communication dans un congrès
Liste complète des métadonnées
Contributeur : Philippe Dessus Connectez-vous pour contacter le contributeur
Soumis le : lundi 17 novembre 2014 - 15:21:41
Dernière modification le : mardi 8 décembre 2020 - 10:38:15
Archivage à long terme le : : vendredi 14 avril 2017 - 16:05:49


Fichiers produits par l'(les) auteur(s)





Mihai Dascalu, Lucia Larise Stavarache, Stefan Trausan-Matu, Philippe Dessus, Maryse Bianco. Reflecting Comprehension through French Textual Complexity Factors. 26th IEEE Int. Conf. on Tools with Artificial Intelligence (ICTAI 2014), Nov 2014, Limassol, Cyprus. pp.615-619, ⟨10.1109/ICTAI.2014.97⟩. ⟨hal-01083594⟩



Consultations de la notice


Téléchargements de fichiers