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This project was carried out by the Natural Language Processing team of Oujda (Oujda-NLP team), from Mohammed I University in Morocco, with the support of the Arab League Educational, Cultural and Scientific Organization (ALECSO).
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Alkhalil Lemmatizer assigns to each word of an Arabic sentence, a single lemma taking into account the word context. The proposed system comprises two modules. The first one consists of an analysis out of context, based on the morphosyntactic analyzer Alkhalil Morpho Sys 2. In the second module, we use the context to identify the correct lemma from the potential lemmas of the word obtained by the first module. For this purpose, we use a statistical technique based on the hidden Markov models, where the observations are the words of the sentence, and the lemmas represent the hidden states. We validate this approach using a labelled corpus consisting of about 500,000 words. The lemmatizer gives the correct lemma in more than 99.24% in the training set and about 94.45% of the words in the test set.

For further details, please check the following paper :

  • M. Boudchiche and A. Mazroui, . “Spline functions for Arabic morphological disambiguation, Applied Computing and Informatics, https://doi.org/10.1016/j.aci.2020.02.002.
  • M. Boudchiche, A. Mazroui, . “A hybrid approach for Arabic lemmatization”, Int. J. Speech Technol., 2018, DOI 10.1007/s10772-018-9528-3.

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