This document provides an overview of various techniques for text categorization, including decision trees, maximum entropy modeling, perceptrons, and K-nearest neighbor classification. It discusses the data representation, model class, and training procedure for each technique. Key aspects covered include feature selection, parameter estimation, convergence criteria, and the advantages/limitations of each approach.