Hybrid approaches for automatic vowelization of Arabic texts

TL;DR

This paper presents a hybrid system combining morphological analysis and statistical modeling with HMMs to improve automatic vowelization of Arabic texts, enhancing natural language processing applications.

Contribution

It introduces a novel hybrid approach integrating morphological analysis with hidden Markov models for more accurate Arabic vowelization.

Findings

Improved vowelization accuracy over previous methods

Effective integration of morphological analysis with HMMs

Potential for enhanced NLP applications in Arabic

Abstract

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.