Automatic readability assessment for people with intellectual disabilities

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.

[1]  Rudolf Franz Flesch,et al.  How to write plain English : a book for lawyers and consumers , 1979 .

[2]  Eugene Charniak,et al.  A Maximum-Entropy-Inspired Parser , 2000, ANLP.

[3]  M. L. Stein,et al.  How to write plain English , 1975 .

[4]  Kathleen McKeown,et al.  Improving Word Sense Disambiguation in Lexical Chaining , 2003, IJCAI.

[5]  R. Gunning.  The Technique of Clear Writing. , 1968 .

[6]  Mari Ostendorf,et al.  Reading Level Assessment Using Support Vector Machines and Statistical Language Models , 2005, ACL.

[7]  Kevyn Collins-Thompson,et al.  A Language Modeling Approach to Predicting Reading Difficulty , 2004, NAACL.

[8]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[9]  W. Finlay,et al.  Assessing the reading comprehension of adults with learning disabilities. , 2006, Journal of intellectual disability research : JIDR.

[10]  Maxine Eskénazi,et al.  Combining Lexical and Grammatical Features to Improve Readability Measures for First and Second Language Texts , 2007, NAACL.

[11]  Luo Si,et al.  A statistical model for scientific readability , 2001, CIKM '01.

[12]  Kentaro Inui,et al.  Text Simplification for Reading Assistance: A Project Note , 2003, IWP@ACL.

[13]  Lijun Feng,et al.  Cognitively Motivated Features for Readability Assessment , 2009, EACL.

[14]  Chih-Jen Lin,et al.  A Practical Guide to Support Vector Classication , 2008 .

[15]  Mari Ostendorf,et al.  A machine learning approach to reading level assessment , 2009, Comput. Speech Lang..

[16]  L. Bilsky,et al.  Comprehension and Mental Retardation , 1985 .

[17]  Susan Edwards,et al.  Language in Mental Retardation , 1996 .

[18]  Devlin Sl,et al.  Simplifying natural language for aphasic readers. , 1999 .