Morphological disambiguation of Tunisian dialect

Inès Zribi (1) , Mariem Ellouze (2) , Lamia Hadrich Belguith (3, 2) , Philippe Blache (1)

1 LPL - Laboratoire Parole et Langage
2 MIRACL - Multimedia, InfoRmation systems and Advanced Computing Laboratory
3 Université de Sfax - University of Sfax

In this paper, we propose a method to disambiguate the output of a morphological analyzer of the Tunisian dialect. We test three machine-learning techniques that classify the morphological analysis of each word token into two classes: true and false. The class label is assigned to each analysis according to the context of the corresponding word in a sentence. In failure cases, we combine the results of the proposed techniques with a bigram classifier to choose only one analysis for a given word. We disam-biguate the result of the morphological analyzer of the Tunisian Dialect Al-Khalil-TUN (Zribi et al., 2013b). We use the Spoken Tunisian Arabic Corpus STAC (Zribi et al., 2015) to train and test our method. The evaluation shows that the proposed method has achieved an accuracy performance of 87.32%. Ó 2017 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Mots clés: Tunisian dialect; Spoken language; Morphological analysis; Morphological disambiguation

Domaines: Informatique [cs]; Informatique et langage [cs.CL]; Traitement du texte et du document

Inès Zribi, Mariem Ellouze, Lamia Hadrich Belguith, Philippe Blache. Morphological disambiguation of Tunisian dialect. Journal of King Saud University - Computer and Information Sciences, 2017, 29 (2), pp.147-155. ⟨10.1016/j.jksuci.2017.01.004⟩. ⟨hal-02869843⟩