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Question classification (QC) is the primary step of the Question Answering(QA) system. Question Classification (QC) system classifies the questions inparticular classes so that Question Answering (QA) System can provide correctanswers for the questions. Our system categorizes the factoid type questionsasked in natural language after extracting features of the questions. Wepresent a two stage QC system for Bengali. It utilizes one dimensionalconvolutional neural network for classifying questions into coarse classes inthe first stage. Word2vec representation of existing words of the questioncorpus have been constructed and used for assisting 1D CNN. A smart databalancing technique has been employed for giving data hungry convolutionalneural network the advantage of a greater number of effective samples to learnfrom. For each coarse class, a separate Stochastic Gradient Descent (SGD) basedclassifier has been used in order to differentiate among the finer classeswithin that coarse class. TF-IDF representation of each word has been used asfeature for the SGD classifiers implemented as part of second stageclassification. Experiments show the effectiveness of our proposed method forBengali question classification.

A Hybrid Approach Towards Two Stage Bengali Question Classification Utilizing Smart Data Balancing Technique