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ReaderBench: A Multi-lingual Framework for Analyzing Text Complexity

  • Mihai Dascalu
  • Gabriel Gutu
  • Stefan Ruseti
  • Ionut Cristian Paraschiv
  • Philippe Dessus
  • Danielle S. McNamara
  • Scott A. Crossley
  • Stefan Trausan-Matu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10474)

Abstract

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.

Keywords

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

Notes

Acknowledgments

This research was partially supported by the FP7 2008-212578 LTfLL project, by the 644187 EC H2020 RAGE project, by the ANR-10-blan-1907-01 DEVCOMP project, as well as by University Politehnica of Bucharest through the “Excellence Research Grants” Program UPB–GEX 12/26.09.2016.

References

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Mihai Dascalu
    • 1
    • 2
  • Gabriel Gutu
    • 1
  • Stefan Ruseti
    • 1
  • Ionut Cristian Paraschiv
    • 1
  • Philippe Dessus
    • 3
  • Danielle S. McNamara
    • 4
  • Scott A. Crossley
    • 5
  • Stefan Trausan-Matu
    • 1
    • 2
  1. 1.University Politehnica of BucharestBucharestRomania
  2. 2.Academy of Romanian ScientistsBucharestRomania
  3. 3.Laboratoire des Sciences de l’ÉducationUniv. Grenoble AlpesGrenobleFrance
  4. 4.Institute for the Science of Teaching and LearningArizona State UniversityTempeUSA
  5. 5.Department of Applied Linguistics/ESLGeorgia State UniversityAtlantaUSA

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