Maximum Entropy Methods
abbreviation(s): MEM
definition: 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.
related person(s):
- Stefan Riezler
- Roni Rosenfeld
- John D. Lafferty
- Adam L. Berger
- Rob Malouf
- Adwait Ratnaparkhi
- Mark Johnson
- Steven Paul Abney
related system(s) / resource(s):
- Mnemonic Predictive Modeling Toolkit
relevant source(s):