A statistical model for scientific readability
Proceedings of the tenth international conference on Information and knowledge management
Predicting reading difficulty with statistical language models
Journal of the American Society for Information Science and Technology
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Reading level assessment using support vector machines and statistical language models
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop
Revisiting readability: a unified framework for predicting text quality
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
An analysis of statistical models and features for reading difficulty prediction
EANL '08 Proceedings of the Third Workshop on Innovative Use of NLP for Building Educational Applications
A comparison of features for automatic readability assessment
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
A posteriori agreement as a quality measure for readability prediction systems
CICLing'11 Proceedings of the 12th international conference on Computational linguistics and intelligent text processing - Volume Part II
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This paper present a new readability formula for French as a foreign language (FFL), which relies on 46 textual features representative of the lexical, syntactic, and semantic levels as well as some of the specificities of the FFL context. We report comparisons between several techniques for feature selection and various learning algorithms. Our best model, based on support vector machines (SVM), significantly outperforms previous FFL formulas. We also found that semantic features behave poorly in our case, in contrast with some previous readability studies on English as a first language.