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.
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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.
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Dascalu, M. et al. (2017). ReaderBench: A Multi-lingual Framework for Analyzing Text Complexity. In: Lavoué, É., Drachsler, H., Verbert, K., Broisin, J., Pérez-Sanagustín, M. (eds) Data Driven Approaches in Digital Education. EC-TEL 2017. Lecture Notes in Computer Science(), vol 10474. Springer, Cham. https://doi.org/10.1007/978-3-319-66610-5_48
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DOI: https://doi.org/10.1007/978-3-319-66610-5_48
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