A Nonstationary Hidden Markov Model with a Hard Capture of Observations: Application to the Problem of Morphological Ambiguities

Contents

This correspondence is concerned with the problem of morphological ambiguities using a Margov process. The problem here is to eliminate interferent solutions that might be derived from a morphological analysis. We start by using a Marlmv chain with one long sequence of transitions. In this model the states are the morphological features and a sequence corresponds to the transition of a word fore from one feature to another. After having observed an inadequacy of this model, one will explore a nonstafionary hidden Markov Wocess. Among the main advantages of this latter model we have the possibility to assign a type to a text given some training samples. Therefore, a recognition of "style" or a aeatim of a new one might be developed.