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

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.
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
PublisherSpringer
Pages495-499
Volume10474
ISBN (Electronic)978-3-319-66610-5
ISBN (Print)978-3-319-66609-9
DOIs
Publication statusPublished - Oct 2017
Externally publishedYes
EventData Driven Approaches in Digital Education: 12th European Conference on Technology Enhanced Learning: EC-TEL - Tallinn, Estonia
Duration: 12 Sep 201715 Sep 2017
http://ectel2017.httc.de/index.php?id=777

Publication series

NameLecture Notes in Computer Science LNCS
PublisherSpringer
Volume10474

Conference

ConferenceData Driven Approaches in Digital Education
CountryEstonia
CityTallinn
Period12/09/1715/09/17
Internet address

Fingerprint

Semantics
Text processing
Syntactics
Information technology
Ontology
Students
Processing

Keywords

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

Cite this

Dascalu, M., Gutu, G., Ruseti, S., Paraschiv, I. C., Dessus, P., McNamara, D. S., ... Trausan-Matu, S. (2017). ReaderBench: A Multi-lingual Framework for Analyzing Text Complexity. In É. Lavoué , H. Drachsler, K. Verbert, J. Broisin, & M. Pérez-Sanagustín (Eds.), Data Driven Approaches in Digital Education. : 12th European Conference on Technology Enhanced Learning (EC-TEL 2017) (Vol. 10474, pp. 495-499). (Lecture Notes in Computer Science LNCS; Vol. 10474). Springer. https://doi.org/10.1007/978-3-319-66610-5_48
Dascalu, Mihai ; Gutu, Gabriel ; Ruseti, Stefan ; Paraschiv, Ionut Cristian ; Dessus, Philippe ; McNamara, Danielle S. ; Crossley, Scott ; Trausan-Matu, Stefan. / ReaderBench: A Multi-lingual Framework for Analyzing Text Complexity. Data Driven Approaches in Digital Education. : 12th European Conference on Technology Enhanced Learning (EC-TEL 2017) . editor / É. Lavoué ; H. Drachsler ; K. Verbert ; J. Broisin ; M. Pérez-Sanagustín. Vol. 10474 Springer, 2017. pp. 495-499 (Lecture Notes in Computer Science LNCS).
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title = "ReaderBench: A Multi-lingual Framework for Analyzing Text Complexity",
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",
author = "Mihai Dascalu and Gabriel Gutu and Stefan Ruseti and Paraschiv, {Ionut Cristian} and Philippe Dessus and McNamara, {Danielle S.} and Scott Crossley and Stefan Trausan-Matu",
year = "2017",
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Dascalu, M, Gutu, G, Ruseti, S, Paraschiv, IC, Dessus, P, McNamara, DS, Crossley, S & Trausan-Matu, S 2017, ReaderBench: A Multi-lingual Framework for Analyzing Text Complexity. in É Lavoué , H Drachsler, K Verbert, J Broisin & M Pérez-Sanagustín (eds), Data Driven Approaches in Digital Education. : 12th European Conference on Technology Enhanced Learning (EC-TEL 2017) . vol. 10474, Lecture Notes in Computer Science LNCS, vol. 10474, Springer, pp. 495-499, Data Driven Approaches in Digital Education, Tallinn, Estonia, 12/09/17. https://doi.org/10.1007/978-3-319-66610-5_48

ReaderBench: A Multi-lingual Framework for Analyzing Text Complexity. / Dascalu, Mihai; Gutu, Gabriel; Ruseti, Stefan; Paraschiv, Ionut Cristian; Dessus, Philippe; McNamara, Danielle S.; Crossley, Scott; Trausan-Matu, Stefan.

Data Driven Approaches in Digital Education. : 12th European Conference on Technology Enhanced Learning (EC-TEL 2017) . ed. / É. Lavoué ; H. Drachsler; K. Verbert; J. Broisin; M. Pérez-Sanagustín. Vol. 10474 Springer, 2017. p. 495-499 (Lecture Notes in Computer Science LNCS; Vol. 10474).

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

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Dascalu M, Gutu G, Ruseti S, Paraschiv IC, Dessus P, McNamara DS et al. ReaderBench: A Multi-lingual Framework for Analyzing Text Complexity. In Lavoué É, Drachsler H, Verbert K, Broisin J, Pérez-Sanagustín M, editors, Data Driven Approaches in Digital Education. : 12th European Conference on Technology Enhanced Learning (EC-TEL 2017) . Vol. 10474. Springer. 2017. p. 495-499. (Lecture Notes in Computer Science LNCS). https://doi.org/10.1007/978-3-319-66610-5_48