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Question classification plays a very important role in Question Answering systems. It gives a label to a question depending on the type of the question. This label will be used by the Answer Extraction module to extract the correct answer. Since there are variety of Natural Language Questions, the task of classifying different questions becomes hard and challenging. Very limited research has been done on classifying Arabic Questions using Machine Learning Techniques. In this paper, we used Support Vector Machines(SVM) and Multinomial Naive Bayes(MNB) to classify Questions. The types of questions classified includes Who, What, Where, When, How many, How much, How and Why. The labels that will be given to these questions respectively are Person/Definition, Location, Time/date, Number/Count, Quantity, Manner and Reason. The SVM showed higher accurate results than MNB. The dataset consisted of 300 questions from the Arabic Wikipedia. The precision of both the SVM and the MNB is equivalent to precision of 1. The achieved F1 measure for SVM is .97 and for the MNB is .95 which is a promising result.

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Assessment of IEC-61499 and CDL for Function Block composition in factory-wide system integration •Date: July, 2013 •Linked to: PLANTCockpit Contact information Tampere University of Technology, FAST Laboratory, P.O. Box 600, FIN-33101 Tampere, Finland Email: fast@tut.fi www.tut.fi/fast Conference: 11th IEEE International Conference on Industrial Informatics, INDIN 2013. Bochum, Germany – July 29-31 2013 Title of the paper: Assessment of IEC-61499 and CDL for Function Block composition in factory-wide system integration Authors: Borja Ramis, Jorge Garcia, Jose L. Martinez Lastra If you would like to receive a reprint of the original paper, please contact us

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