ReaderBench: A multi-lingual framework for analyzing text complexity

Mihai Dascalu, Gabriel Gutu, Stefan Ruseti, Ionut Cristian Paraschiv, Philippe Dessus, Danielle McNamara, Scott A. Crossley, Stefan Trausan-Matu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationData Driven Approaches in Digital Education - 12th European Conference on Technology Enhanced Learning, EC-TEL 2017, Proceedings
EditorsJulien Broisin, Elise Lavoue, Hendrik Drachsler, Katrien Verbert, Mar Perez-Sanagustin
PublisherSpringer Verlag
Number of pages5
ISBN (Print)9783319666099
StatePublished - 2017
Event12th European Conference on Technology Enhanced Learning, EC-TEL 2017 - Tallinn, Estonia
Duration: Sep 12 2017Sep 15 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10474 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other12th European Conference on Technology Enhanced Learning, EC-TEL 2017


  • Comprehension prediction
  • Multi-lingual text analysis
  • Natural Language Processing
  • Textual cohesion
  • Textual complexity
  • Writing style

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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