Maximum entropy methods are techniques for the estimation of probability distributions that pick the "most uniform" distribution compatible with the observed statistics. The maximum entropy formulation has a unique solution which can be found by iterative scaling algorithms. Maximum entropy models have been applied to NLP-related task like text segmentation and classification, language modeling, part-of-speech tagging, parsing, and machine translation.