Making large-scale support vector machine learning practical
Advances in kernel methods
Support vector machine learning for interdependent and structured output spaces
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
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
A comparison of features for automatic readability assessment
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
PITR '12 Proceedings of the First Workshop on Predicting and Improving Text Readability for target reader populations
What to read next?: making personalized book recommendations for K-12 users
Proceedings of the 7th ACM conference on Recommender systems
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Determining the reading level of children's literature is an important task for providing educators and parents with an appropriate reading trajectory through a curriculum. Automating this process has been a challenge addressed before in the computational linguistics literature, with most studies attempting to predict the particular grade level of a text. However, guided reading levels developed by educators operate at a more fine-grained level, with multiple levels corresponding to each grade. We find that ranking performs much better than classification at the fine-grained leveling task, and that features derived from the visual layout of a book are just as predictive as standard text features of level; including both sets of features, we find that we can predict the reading level up to 83% of the time on a small corpus of children's books.