Intro -- Abstract -- Acknowledgments -- Contents -- List of Figures -- List of Tables -- Introduction -- Background and related work -- Text complexity analysis -- Machine learning -- Features for text complexity -- Shallow text features -- Lexical features -- Morpho-syntactic features -- Syntactic features -- Necessary pre-processing -- Classification using the feature set -- Introduction -- Method -- Evaluation of features for easy-to-read classification -- Choosing optimal features for easy-to-read classification -- Discussion -- Summary -- A note on the philosophy of statistics and probability -- Probability, statistics and data -- Thomas Bayes, Pierre-Simon Laplace and their theorem -- Illustrative criticisms -- Why I am a Bayesian -- Statistical models for text complexity assessment -- The Multi-Scale Probit model -- Evaluation procedure -- Simulation experiments -- Application to text complexity analysis -- Closing words -- Future work and final words -- Future work -- Conclusion -- Bibliography -- Supplementary figures -- Posterior distribution for bold0mu mumu 2005/06/28 ver: 1.3 subfig package -- Publications -- Department of Computer and Information Science.