  Hybrid approaches for automatic vowelization of Arabic texts are presented in
this article. The process is made up of two modules. In the first one, a
morphological analysis of the text words is performed using the open source
morphological Analyzer AlKhalil Morpho Sys. Outputs for each word analyzed out
of context, are its different possible vowelizations. The integration of this
Analyzer in our vowelization system required the addition of a lexical database
containing the most frequent words in Arabic language. Using a statistical
approach based on two hidden Markov models (HMM), the second module aims to
eliminate the ambiguities. Indeed, for the first HMM, the unvowelized Arabic
words are the observed states and the vowelized words are the hidden states.
The observed states of the second HMM are identical to those of the first, but
the hidden states are the lists of possible diacritics of the word without its
Arabic letters. Our system uses Viterbi algorithm to select the optimal path
among the solutions proposed by Al Khalil Morpho Sys. Our approach opens an
important way to improve the performance of automatic vowelization of Arabic
texts for other uses in automatic natural language processing.
