Abstract
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.
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References
Khoja, S.: APT: Arabic part-of-speech tagger. In: Proceedings of Student Workshop at the Second Meeting of the North American Association for Computational Linguistics. Carnegie Mellon University, Pennsylvania (2001)
Hajic, J.: Morphological Tagging: Data vs. Dictionaries. In: Proceedings of the 1st North American Chapter of the Association for Computational Linguistics Conference, pp. 94–101. Association for Computational Linguistics, Stroudsburg (2000)
Roth, R., Rambow, O., Habash, N., Diab, M., Rudin, C.: Arabic Morphological Tagging, Diacritization, and Lemmatization Using Lexeme Models and Feature Ranking. In: Proceedings of the Association for Computational Linguistics Conference (ACL), Columbus, Ohio, USA, pp. 117–120 (2008)
Habash, N., Rambow, O.: Arabic Tokenization, Part-of-speech Tagging and Morphological Disambiguation in One Fell Swoop. In: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, Stroudsburg, PA, USA, pp. 573–580 (2005)
Habash, N., Rambow, O.: Arabic DiacritizationThrough Full Morphological Tagging. In: Human Language Technologies: The Conference of the North American Chapter of the Association for Computational Linguistics, Stroudsburg, PA, USA, pp. 53–56 (2007)
Ayed, R., Bounhas, I., Elayeb, B., Evrard, F.: Bellamine Ben Saoud, N.: A Possibilistic Approach for the Automatic Morphological Disambiguation of Arabic Texts. In: Hochin, T., Lee, R. (eds.) Proceedings of the 13th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel Distributed Computing (SNPD), Kyoto, Japan, pp. 187–194 (2012)
Ayed, R., Bounhas, I., Elayeb, B., Evrard, F., Saoud, N.B.B.: Arabic Morphological Analysis and Disambiguation Using a Possibilistic Classifier. In: Huang, D.-S., Ma, J., Jo, K.-H., Gromiha, M.M. (eds.) ICIC 2012. LNCS (LNAI), vol. 7390, pp. 274–279. Springer, Heidelberg (2012)
Haouari, B., Ben Amor, N., Elouedi, Z., Mellouli, K.: Naïve Possibilistic Network Classifiers. Fuzzy Sets and Systems 160(22), 3224–3238 (2009)
Dubois, D., Prade, H.: Possibility Theory: An Approach to computerized Processing of Uncertainty. Plenum Press, New York (1994)
Dubois, D.J., Prade, H.: Théorie des possibilités: applications à la représentation des connaissances en informatique. Masson, Paris (1985)
Dubois, D., Prade, H.: Possibility Theory: Qualitative and Quantitative Aspects. In: Gabbay, D.M., Smets, P. (eds.) Handbook on Defeasible Reasoning and Uncertainty Management Systems, pp. 169–226. Kluwer Academic, Springer, Dordrecht, Netherlands (1998)
Alkuhlani, S., Habash, N., Roth, R.: Automatic Morphological Enrichment of a Morphologically Underspecified Treebank. In: Clemmer, A., Post, M. (eds.) Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, HLT-NAACL, pp. 460–470. Omnipress of Madison, Wisconsin (2013)
Bounhas, M., Mellouli, K., Prade, H., Serrurier, M.: Possibilistic classifiers for numerical data. Soft Computing 17(5), 733–751 (2013)
Buckwalter, T.: BuckwalterArabicMorphological Analyzer Version 2.0. Linguistic Data Consortium (LDC) catalogue number LDC2004L02 (2004) ISBN 1-58563-324-0
Raghavan, H., Allan, J.: An interactive algorithm for asking and incorporating feature feedback into support vector machines. In: ACM SIGIR Conference (2007)
Pearl, J.: Probabilistic reasoning in intelligent systems: networks of plausible inference. In: Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmman, San Francisco (1988)
Quinlan, R.: Induction of decision trees. Machine Learning 1, 81–106 (1986)
Harrag, F., Hamdi-Cherif, A., Malik, A., Al-Salman, S., El-Qawasmeh, E.: Experiments in Improvement of Arabic Information Retrieval. In: Proc. 3rd International Conference on Arabic Language Processing (CITALA), Rabat, Morocco, pp. 71–81 (2009)
Vapnik, V.: Statistical Learning Theory, pp. 1–736. Wiley, New York (1998)
Aggarwal, C.C., Changing, Z.: A survey of text classification algorithms. In: Mining Text Data, pp. 163–213 (2012)
Mesleh, A.: Support Vector Machines based Arabic Language Text Classification System: Feature Selection Comparative Study. In: 12th WSEAS International Conference on applied mathematics, Cairo, Egypt, pp. 11–16 (2007)
Al-Echikh, A.A.: Encyclopedia of the six major citation collections. Daresselem, Ryadh (1998)
Jbara, K.: Knowledge Discovery in Al-Hadith Using Text Classification Algorithm. Journal of American Science 6(11), 409–419 (2010)
Alkhatib, M.: Classification of Al-Hadith Al-Shareef Using Data Mining Algorithm. In: Proceedings of European, Mediterranean & Middle Eastern Conference on Information Systems, Abu Dhabi (2010)
Bounhas, I., Elayeb, B., Evrard, F., Slimani, Y.: Organizing Contextual Knowledge for Arabic Text Disambiguation and Terminology Extraction. Knowledge Organization 38(6), 473–490 (2011)
Outahajala, M., Benajiba, Y., Rosso, P., Zenkouar, L.: POS Tagging in Amazighe Using Support Vector Machines and Conditional Random Fields. In: Muñoz, R., Montoyo, A., Métais, E. (eds.) NLDB 2011. LNCS, vol. 6716, pp. 238–241. Springer, Heidelberg (2011)
Georgescul, M., Rayner, M., Bouillon, P.: Spoken Language Understanding via Supervised Learning and Linguistically Motivated Features. In: Hopfe, C.J., Rezgui, Y., Métais, E., Preece, A., Li, H. (eds.) NLDB 2010. LNCS, vol. 6177, pp. 117–128. Springer, Heidelberg (2010)
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Ayed, R., Bounhas, I., Elayeb, B., Ben Saoud, N.B., Evrard, F. (2014). Improving Arabic Texts Morphological Disambiguation Using a Possibilistic Classifier. In: Métais, E., Roche, M., Teisseire, M. (eds) Natural Language Processing and Information Systems. NLDB 2014. Lecture Notes in Computer Science, vol 8455. Springer, Cham. https://doi.org/10.1007/978-3-319-07983-7_18
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DOI: https://doi.org/10.1007/978-3-319-07983-7_18
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