A statistical model for scientific readability
Proceedings of the tenth international conference on Information and knowledge management
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Text simplification for reading assistance: a project note
PARAPHRASE '03 Proceedings of the second international workshop on Paraphrasing - Volume 16
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
Cognitively motivated features for readability assessment
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Improving word sense disambiguation in lexical chaining
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Web page classification on child suitability
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
A combined topical/non-topical approach to identifying web sites for children
Proceedings of the fourth ACM international conference on Web search and data mining
Quantitative evaluation of grammaticality of summaries
CICLing'10 Proceedings of the 11th international conference on Computational Linguistics and Intelligent Text Processing
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My research goal is to advance our understanding of, and quantify, what makes a text easy or difficult to read, in particular for readers with intellectual disabilities. Previous research in automatic readability assessment has looked at a limited class of lexi-cal and syntactic properties of texts. Moreover, these models are usually not targeted towards any particular group of readers. In my own work, by contrast, I have used sophisticated computational tools to build an automatic readability metric that exploits global semantic (discourse level) properties of a text, in addition to well-studied lexical and syntactic features. Our preliminary results (Feng et al., 2009) confirm the value of discourse attributes. My research is targeted towards understanding the particular difficulties faced by readers with intellectual disabilities. The ultimate goal is not simply to model and understand readability issues, but also to aide in the development of automatic language processing tools that can rewrite texts to be more readable.