ReaderBench: A Multi-lingual Framework for Analyzing Text Complexity

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

Research output: Chapter in Book/Report/Conference proceedingConference Article in proceedingAcademicpeer-review


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
Title of host publicationData Driven Approaches in Digital Education.
Subtitle of host publication12th European Conference on Technology Enhanced Learning (EC-TEL 2017)
EditorsÉ. Lavoué , H. Drachsler, K. Verbert, J. Broisin, M. Pérez-Sanagustín
ISBN (Electronic)978-3-319-66610-5
ISBN (Print)978-3-319-66609-9
Publication statusPublished - Oct 2017
Externally publishedYes
Event12th European Conference on Technology Enhanced Learning: Data Driven Approaches in Digital Education - Tallinn, Estonia
Duration: 12 Sept 201715 Sept 2017

Publication series

SeriesLecture Notes in Computer Science LNCS


Conference12th European Conference on Technology Enhanced Learning
Abbreviated titleEC-TEL 2017
Internet address


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


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