ReaderBench: A Multi-lingual Framework for Analyzing Text Complexity

Mihai A Dascalu (1) , Gabriel S Gutu (1) , Stefan S Ruseti (1) , Ionut S Cristian Paraschiv (1) , Philippe Dessus (2) , Danielle A Mcnamara (3) , Scott A Crossley (4) , Stefan A Trausan-Matu (1)

1 Université Politehnica
2 LaRAC - Laboratoire de Recherche sur les Apprentissages en Contexte
3 ASU - Arizona State University [Tempe]
4 University of Georgia [USA]

Assessing textual complexity is a difficult, but important endeavor, especially for adapting learning materials to students' and readers' levels of understanding. With the continuous growth of information technologies spanning through various research fields, automated assessment tools have become reliable solutions to automatically assessing textual complexity. ReaderBench is a text processing framework relying on advanced Natural Language Processing techniques that encompass a wide range of text analysis modules available in a variety of languages, including English, French, Romanian, and Dutch. To our knowledge, ReaderBench is the only open-source multilingual textual analysis solution that provides unified access to more than 200 textual complexity indices including: surface, syntactic, morphological, semantic, and discourse specific factors, alongside cohesion metrics derived from specific lexicalized ontologies and semantic models.

Mots clés: Multi-Lingual Text Analysis; Textual Complexity; Comprehension Prediction; Natural Language Processing; Textual Cohesion; Writing Style

Domaines: Education; Environnements Informatiques pour l'Apprentissage Humain

Mihai A Dascalu, Gabriel S Gutu, Stefan S Ruseti, Ionut S Cristian Paraschiv, Philippe Dessus, et al.. ReaderBench: A Multi-lingual Framework for Analyzing Text Complexity. Data Driven Approaches in Digital Education, Proc 12th European Conference on Technology Enhanced Learning, EC-TEL 2017, 2017, Tallinn, Estonia. pp.606-609, ⟨10.1007/978-3-319-61425-0_5⟩. ⟨hal-01584870⟩