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

doi:10.1007/978-3-319-66610-5_48

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 proceeding › Conference Article in proceeding › Academic › peer-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 language	English
Title of host publication	Data Driven Approaches in Digital Education.
Subtitle of host publication	12th European Conference on Technology Enhanced Learning (EC-TEL 2017)
Editors	É. Lavoué , H. Drachsler, K. Verbert, J. Broisin, M. Pérez-Sanagustín
Publisher	Springer
Pages	495-499
Volume	10474
ISBN (Electronic)	978-3-319-66610-5
ISBN (Print)	978-3-319-66609-9
DOIs	https://doi.org/10.1007/978-3-319-66610-5_48
Publication status	Published - Oct 2017
Externally published	Yes
Event	12th European Conference on Technology Enhanced Learning: Data Driven Approaches in Digital Education - Tallinn, Estonia Duration: 12 Sept 2017 → 15 Sept 2017 http://ectel2017.httc.de/index.php?id=777

Publication series

Series	Lecture Notes in Computer Science LNCS
Volume	10474

Conference

Conference	12th European Conference on Technology Enhanced Learning
Abbreviated title	EC-TEL 2017
Country/Territory	Estonia
City	Tallinn
Period	12/09/17 → 15/09/17
Internet address	http://ectel2017.httc.de/index.php?id=777

Keywords

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

Access to Document

10.1007/978-3-319-66610-5_48Licence: CC BY-NC-ND

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Westera, W. (PI), Georgiadis, K. (CoI), Saveski, G. (CoI), van Lankveld, G. (CoI), Bahreini, K. (CoI), van der Vegt, W. (CoI), Berkhout, J. (CoI), Nyamsuren, E. (CoI), Kluijfhout, E. (CoI) & Nadolski, R. (CoI)
European Union
1/02/15 → 31/07/19
Project: Research

Cite this

Dascalu, M., Gutu, G., Ruseti, S., Paraschiv, I. C., Dessus, P., McNamara, D. S., 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, pp. 495-499). Springer. https://doi.org/10.1007/978-3-319-66610-5_48

Dascalu, Mihai ; Gutu, Gabriel ; Ruseti, Stefan et al. / 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, Vol. 10474).

@inproceedings{26d8e5b738e64e37be75ee4921afa9f5,

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{\textquoteright} and readers{\textquoteright} 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",

month = oct,

doi = "10.1007/978-3-319-66610-5_48",

language = "English",

isbn = "978-3-319-66609-9",

volume = "10474",

series = "Lecture Notes in Computer Science LNCS",

publisher = "Springer",

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editor = "{Lavou{\'e} }, {{\'E}. } and Drachsler, {H. } and K. Verbert and J. Broisin and M. P{\'e}rez-Sanagust{\'i}n",

booktitle = "Data Driven Approaches in Digital Education.",

note = "12th European Conference on Technology Enhanced Learning : Data Driven Approaches in Digital Education, EC-TEL 2017 ; Conference date: 12-09-2017 Through 15-09-2017",

url = "http://ectel2017.httc.de/index.php?id=777",

}

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, Springer, Lecture Notes in Computer Science LNCS, vol. 10474, pp. 495-499, 12th European Conference on Technology Enhanced Learning, 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 et al.
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 proceeding › Conference Article in proceeding › Academic › peer-review

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AU - Gutu, Gabriel

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AU - McNamara, Danielle S.

AU - Crossley, Scott

AU - Trausan-Matu, Stefan

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AB - 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.

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KW - Writing style

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ER -

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, Vol. 10474). doi: 10.1007/978-3-319-66610-5_48

ReaderBench: A Multi-lingual Framework for Analyzing Text Complexity

Abstract

Publication series

Conference

Keywords

Access to Document

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