TY - GEN
T1 - ReaderBench
T2 - 12th European Conference on Technology Enhanced Learning, EC-TEL 2017
AU - Dascalu, Mihai
AU - Gutu, Gabriel
AU - Ruseti, Stefan
AU - Paraschiv, Ionut Cristian
AU - Dessus, Philippe
AU - McNamara, Danielle
AU - Crossley, Scott A.
AU - Trausan-Matu, Stefan
N1 - Funding Information:
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.
Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - 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.
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.
KW - Comprehension prediction
KW - Multi-lingual text analysis
KW - Natural Language Processing
KW - Textual cohesion
KW - Textual complexity
KW - Writing style
UR - http://www.scopus.com/inward/record.url?scp=85029603796&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85029603796&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-66610-5_48
DO - 10.1007/978-3-319-66610-5_48
M3 - Conference contribution
AN - SCOPUS:85029603796
SN - 9783319666099
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 495
EP - 499
BT - Data Driven Approaches in Digital Education - 12th European Conference on Technology Enhanced Learning, EC-TEL 2017, Proceedings
A2 - Broisin, Julien
A2 - Lavoue, Elise
A2 - Drachsler, Hendrik
A2 - Verbert, Katrien
A2 - Perez-Sanagustin, Mar
PB - Springer Verlag
Y2 - 12 September 2017 through 15 September 2017
ER -