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Please use this identifier to cite or link to this item: http://hdl.handle.net/1820/8455
Title: ReaderBench: A Multi-lingual Framework for Analyzing Text Complexity
Authors: Dascalu, Mihai
Gutu, Gabriel
Ruseti, Stefan
Paraschiv, Ionut Cristian
Dessus, Philippe
McNamara, Danielle
Crossley, Scott
Trausan-Matu, Stefan
Keywords: Multi-lingual text analysis
Textual complexity
Comprehension prediction
Natural Language Processing
Textual cohesion
Writing style
Issue Date: Oct-2017
Publisher: Springer
Citation: 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 E. Lavoué, H. Drachsler, K. Verbert, J. Broisin & M. Pérez-Sanagustín (Eds.), 12th European Conference on Technology Enhanced Learning (EC-TEL 2017) (pp. 495–499). Tallinn, Estonia: Springer.
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.
URI: http://hdl.handle.net/1820/8455
Appears in Collections:1. RAGE Publications

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