@inproceedings{wilkens-etal-2022-fabra,
title = "{FABRA}: {F}rench Aggregator-Based Readability Assessment toolkit",
author = {Wilkens, Rodrigo and
Alfter, David and
Wang, Xiaoou and
Pintard, Alice and
Tack, Ana{\"\i}s and
Yancey, Kevin P. and
Fran{\c{c}}ois, Thomas},
editor = "Calzolari, Nicoletta and
B{\'e}chet, Fr{\'e}d{\'e}ric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H{\'e}l{\`e}ne and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lrec-1.130",
pages = "1217--1233",
abstract = "In this paper, we present the FABRA: readability toolkit based on the aggregation of a large number of readability predictor variables. The toolkit is implemented as a service-oriented architecture, which obviates the need for installation, and simplifies its integration into other projects. We also perform a set of experiments to show which features are most predictive on two different corpora, and how the use of aggregators improves performance over standard feature-based readability prediction. Our experiments show that, for the explored corpora, the most important predictors for native texts are measures of lexical diversity, dependency counts and text coherence, while the most important predictors for foreign texts are syntactic variables illustrating language development, as well as features linked to lexical sophistication. FABRA: have the potential to support new research on readability assessment for French.",
}
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%0 Conference Proceedings
%T FABRA: French Aggregator-Based Readability Assessment toolkit
%A Wilkens, Rodrigo
%A Alfter, David
%A Wang, Xiaoou
%A Pintard, Alice
%A Tack, Anaïs
%A Yancey, Kevin P.
%A François, Thomas
%Y Calzolari, Nicoletta
%Y Béchet, Frédéric
%Y Blache, Philippe
%Y Choukri, Khalid
%Y Cieri, Christopher
%Y Declerck, Thierry
%Y Goggi, Sara
%Y Isahara, Hitoshi
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Mazo, Hélène
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Thirteenth Language Resources and Evaluation Conference
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F wilkens-etal-2022-fabra
%X In this paper, we present the FABRA: readability toolkit based on the aggregation of a large number of readability predictor variables. The toolkit is implemented as a service-oriented architecture, which obviates the need for installation, and simplifies its integration into other projects. We also perform a set of experiments to show which features are most predictive on two different corpora, and how the use of aggregators improves performance over standard feature-based readability prediction. Our experiments show that, for the explored corpora, the most important predictors for native texts are measures of lexical diversity, dependency counts and text coherence, while the most important predictors for foreign texts are syntactic variables illustrating language development, as well as features linked to lexical sophistication. FABRA: have the potential to support new research on readability assessment for French.
%U https://aclanthology.org/2022.lrec-1.130
%P 1217-1233
Markdown (Informal)
[FABRA: French Aggregator-Based Readability Assessment toolkit](https://aclanthology.org/2022.lrec-1.130) (Wilkens et al., LREC 2022)
ACL
- Rodrigo Wilkens, David Alfter, Xiaoou Wang, Alice Pintard, Anaïs Tack, Kevin P. Yancey, and Thomas François. 2022. FABRA: French Aggregator-Based Readability Assessment toolkit. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 1217–1233, Marseille, France. European Language Resources Association.