Abstract Question Answering Systems (QAS) aim at returning precise answers to user’s questions that are written in natural language. In this paper, we describe our question processing and document retrieval as two components of Arabic QAS. First, we present Arabic question classification method based on SVM classifier and Li and Roth’s [24] taxonomy. Then, we describe our proposed technique to transform an Arabic question, to a query which is available to get information from the Arabic Wikipedia. In this paper, we use a hybrid Arabic Part-of-Speech (POS) tagging and Arabic WordNet (AWN) for query expansion. We have conducted several experiments using Text Retrieval Conference (TREC) and Cross Lingual Evaluation Forum (CLEF) datasets. The obtained results have shown that the proposed method is more effective as compared with the existing methods.

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[30] Trigui O., Belguith L., and Rosso P., DefArabicQA: Arabic Definition Question Answering System, in Proceedings of the 7th Workshop on Language Resources and Human Language Technologies for Semitic Languages, Valletta, pp. 40-45, 2010. Toward a New Arabic Question Answering System 619 Imane Lahbari is a Phd student in Laboratory of Informatics and Modeling, Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, Fez, Morocco. She received the master degree in Information Systems, Network and Multimedia in 2015. Her research interests include Natural language processing and Question Answering Systems. Said El Alaoui is working as a Professor since 1997 in Department of Computer Science, Faculty of Sciences Dhar EL-Mahraz (FSDM) at Sidi Mohamed Ben Abdellah University (USMBA), Fez, Morocco. His current research interests include Natural Language Processing, Information Retrieval, Biomedical Question Answering, Biomedical Information Extraction, and Arabic Document Clustering and Categorization, High-dimensional indexing and Content-Based Image Retrieval. Khalid Zidani received his PhD degree from the Faculty of Sciences, Nantes, France, in 1994 in computer Science. His current research interests include Natural Language Processing, Arabic Text Mining, Information Retrieval, Document Clustering and Categorization, Content-Based Image Retrieval, Large Image Databases Indexing.