A statistical model for scientific readability
Proceedings of the tenth international conference on Information and knowledge management
Reading level assessment using support vector machines and statistical language models
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
A machine learning approach to reading level assessment
Computer Speech and Language
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
Retrieval of reading materials for vocabulary and reading practice
EANL '08 Proceedings of the Third Workshop on Innovative Use of NLP for Building Educational Applications
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Readability assessment for text simplification
IUNLPBEA '10 Proceedings of the NAACL HLT 2010 Fifth Workshop on Innovative Use of NLP for Building Educational Applications
Learning to predict readability using diverse linguistic features
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
A comparison of features for automatic readability assessment
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Automatic readability assessment
Automatic readability assessment
READ-IT: assessing readability of Italian texts with a view to text simplification
SLPAT '11 Proceedings of the Second Workshop on Speech and Language Processing for Assistive Technologies
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We investigate the problem of readability assessment using a range of lexical and syntactic features and study their impact on predicting the grade level of texts. As empirical basis, we combined two web-based text sources, Weekly Reader and BBC Bitesize, targeting different age groups, to cover a broad range of school grades. On the conceptual side, we explore the use of lexical and syntactic measures originally designed to measure language development in the production of second language learners. We show that the developmental measures from Second Language Acquisition (SLA) research when combined with traditional readability features such as word length and sentence length provide a good indication of text readability across different grades. The resulting classifiers significantly outperform the previous approaches on readability classification, reaching a classification accuracy of 93.3%.