  The ability to take into account the characteristics - also called features -
of observations is essential in Natural Language Processing (NLP) problems.
Hidden Markov Chain (HMC) model associated with classic Forward-Backward
probabilities cannot handle arbitrary features like prefixes or suffixes of any
size, except with an independence condition. For twenty years, this default has
encouraged the development of other sequential models, starting with the
Maximum Entropy Markov Model (MEMM), which elegantly integrates arbitrary
features. More generally, it led to neglect HMC for NLP. In this paper, we show
that the problem is not due to HMC itself, but to the way its restoration
algorithms are computed. We present a new way of computing HMC based
restorations using original Entropic Forward and Entropic Backward (EFB)
probabilities. Our method allows taking into account features in the HMC
framework in the same way as in the MEMM framework. We illustrate the
efficiency of HMC using EFB in Part-Of-Speech Tagging, showing its superiority
over MEMM based restoration. We also specify, as a perspective, how HMCs with
EFB might appear as an alternative to Recurrent Neural Networks to treat
sequential data with a deep architecture.
