Computational Linguistics

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Al-Chalabi, H., S. Ray, and K. Shaalan, "Semantic Based Query Expansion for Arabic Question Answering Systems", The International Conference on Arabic Computational Linguistics (ACLing), Cairo, Egypt, 17 April, 2015. Abstractqeacling2015.pdf

Question Answering Systems have emerged as a good alternative to search engines where they produce the desired information in a very precise way in the real time. However, one serious concern with the Question Answering system is that despite having answers of the questions in the knowledge base, they are not able to retrieve the answer due to mismatch between the words used by users and content creators. There has been a
lot of research in the field of English and some European language Question Answering Systems to handle this issue. However, Arabic Question Answering Systems could not match the pace due to some inherent difficulties with the language itself as well as due to lack of tools available to assist the researchers. In this paper, we are
presenting a method to add semantically equivalent keywords in the questions by using semantic resources. The experiments suggest that the proposed research can deliver highly accurate
answers for Arabic questions.

Atia, S., and K. Shaalan, "Increasing the Accuracy of Opinion Mining in Arabic", The International Conference on Arabic Computational Linguistics (ACLing), Cairo, Egypt, 18 April, 2015. Abstractopinionminacling2015.pdf

Opinion Mining is a raising research field of interest, with its different applications derived by market needs
to analyze product reviews or to assess the public opinion, for political reasons, during presidential campaigns. In this paper, we address an approach for improving accuracy of Opinion Mining in Arabic. In order to conduct our study we need Arabic linguistic resources for opinion mining. Investigating the available resources we found that the OCA corpus is available and sufficient to prove our approach. Experimental results showed that applying different parameters of the machine learning classifiers on the OCA corpus leads to increasing the accuracy of the Arabic Opinion Mining.