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The Arabic language differs from other natural languages in its structures and compositions. In this article we have developed an Arabic morphological analyzer. For this, we have used the relational concept in the database to build our Arabic morphological analyzer. This analyzer uses a set of tables which are linked together by relationships. These relations model certain numbers of compatibility rules between different affixes. Our morphological analysis have been trained and tested on the same databases. The tests of our new approach have given good results and the numbers obtained are very close to those of existing analyzer.

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Correcting spelling errors based on the context is a fairly significant problem in Natural Language Processing (NLP) applications. The majority of the work carried out to introduce the context into the process of spelling correction uses the n-gram language models. However, these models fail in several cases to give adequate probabilities for the suggested solutions of a misspelled word in a given context. To resolve this issue, we propose two new language models inspired by stochastic language models combined with edit distance. A first phase consists in finding the words of the lexicon orthographically close to the erroneous word and a second phase consists in ranking and limiting these suggestions. We have applied the new approach to Arabic language taking into account its specificity of having strong contextual connections between distant words in a sentence. To evaluate our approach, we have developed textual data processing applications, namely the extraction of distant transition dictionaries. The correction accuracy obtained exceeds 98% for the first 10 suggestions. Our approach has the advantage of simplifying the parameters to be estimated with a higher correction accuracy compared to n-gram language models. Hence the need to use such an approach.