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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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PROFESSIONAL PROFILE

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As technical communicators, we touch every product, process, and service on this planet (and off of it). Without us, governments would fall, inventors would have a hard time getting their products to users, and patients would die. In short, no product, process, or service would function very well. Yet, because our work is part of the infrastructure, we often don’t hear about how we’re doing until something goes wrong. Kit Brown-Hoekstra takes a look at how we can act as catalysts for positive change in the world, just by doing our jobs well. Merriam-Webster’s dictionary defines a catalyst as “an agent that provokes or speeds significant change or action.” During this talk, we will look at different kinds of change,and how we, as technical communicators, can influence and change processes to make our world a better place.

Communication Catalysts: Changing the World by Doing Our Jobs

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