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
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
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
Personalizing web search results by reading level
Proceedings of the 20th ACM international conference on Information and knowledge management
Characterizing web content, user interests, and search behavior by reading level and topic
Proceedings of the fifth ACM international conference on Web search and data mining
Building readability lexicons with unannotated corpora
PITR '12 Proceedings of the First Workshop on Predicting and Improving Text Readability for target reader populations
Modeling dwell time to predict click-level satisfaction
Proceedings of the 7th ACM international conference on Web search and data mining
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Models of language learning play a central role in a wide range of applications: from psycholinguistic theories of how people acquire new word knowledge, to information systems that can automatically match content to users' reading ability. We present a novel statistical approach that can infer the distribution of a word's likely acquisition age automatically from authentic texts collected from the Web. We then show that combining these acquisition age distributions for all words in a document provides an effective semantic component for predicting reading difficulty of new texts. We also compare our automatically inferred acquisition ages with norms from existing oral studies, revealing interesting historical trends as well as differences between oral and written word acquisition processes.