The nature of statistical learning theory
The nature of statistical learning theory
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Predicting reading difficulty with statistical language models
Journal of the American Society for Information Science and Technology
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
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
Statistical estimation of word acquisition with application to readability prediction
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
EUSUM: extracting easy-to-understand english summaries for non-native readers
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Readability assessment for text simplification
IUNLPBEA '10 Proceedings of the NAACL HLT 2010 Fifth Workshop on Innovative Use of NLP for Building Educational Applications
Predicting cloze task quality for vocabulary training
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
Revisiting the readability assessment of texts in Portuguese
IBERAMIA'10 Proceedings of the 12th Ibero-American conference on Advances in artificial intelligence
Readability annotation: replacing the expert by the crowd
IUNLPBEA '11 Proceedings of the 6th Workshop on Innovative Use of NLP for Building Educational Applications
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
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP
Do NLP and machine learning improve traditional readability formulas?
PITR '12 Proceedings of the First Workshop on Predicting and Improving Text Readability for target reader populations
An "AI readability" formula for French as a foreign language
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Customizing search results for non-native speakers
Proceedings of the 21st ACM international conference on Information and knowledge management
Assessing user-specific difficulty of documents
Information Processing and Management: an International Journal
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
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A reading difficulty measure can be described as a function or model that maps a text to a numerical value corresponding to a difficulty or grade level. We describe a measure of readability that uses a combination of lexical features and grammatical features that are derived from subtrees of syntactic parses. We also tested statistical models for nominal, ordinal, and interval scales of measurement. The results indicate that a model for ordinal regression, such as the proportional odds model, using a combination of grammatical and lexical features is most effective at predicting reading difficulty.