Improving Arabic Texts Morphological Disambiguation using Possibilistic Classifier (NLDB 2014)
| Autor: | Ayed, Raja, Bounhas, Ibrahim, Elayeb, Bilel, Bellamine Ben Saoud, Narjes, Evrard, Fabrice |
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| Přispěvatelé: | Laboratoire de recherche en Génie Logiciel, Applications distribuées, Systèmes décisionnels et Imagerie intelligente [Manouba] (RIADI), École Nationale des Sciences de l'Informatique [Manouba] (ENSI), Université de la Manouba [Tunisie] (UMA)-Université de la Manouba [Tunisie] (UMA), Institut Supérieur de Documentation [Manouba] (ISD), Université de la Manouba [Tunisie] (UMA), Logique, Interaction, Langue et Calcul (IRIT-LILaC), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, Elisabeth Métais, Mathieu Roche, Maguelonne Teisseire |
| Jazyk: | angličtina |
| Rok vydání: | 2014 |
| Předmět: | |
| Zdroj: | Natural Language Processing and Information Systems: 19th International Conference on Applications of Natural Language to Information Systems, NLDB 2014, Montpellier, France, June 18-20, 2014. Proceedings ; ISBN: 978-3-319-07983-7 19th International Conference on Application of Natural Language to Information Systems (NLDB 2014) 19th International Conference on Application of Natural Language to Information Systems (NLDB 2014), Jun 2014, Montpellier, France. pp.138--147, ⟨10.1007/978-3-319-07983-7_18⟩ |
| DOI: | 10.1007/978-3-319-07983-7_18⟩ |
| Popis: | International audience; Morphological ambiguity is an important problem that has been studied through different approaches. We investigate, in this paper, some classification methods to disambiguate Arabic morphological features of non-vocalized texts. A possibilistic approach is improved and proposed to handle imperfect training and test datasets. We introduce a data transformation method to convert the imperfect dataset to a perfect one. We compare the disambiguation results of classification approaches to results given by the possibilistic classifier dealing with imperfection context. |
| Databáze: | OpenAIRE |
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