  Question classification (QC) is the primary step of the Question Answering
(QA) system. Question Classification (QC) system classifies the questions in
particular classes so that Question Answering (QA) System can provide correct
answers for the questions. Our system categorizes the factoid type questions
asked in natural language after extracting features of the questions. We
present a two stage QC system for Bengali. It utilizes one dimensional
convolutional neural network for classifying questions into coarse classes in
the first stage. Word2vec representation of existing words of the question
corpus have been constructed and used for assisting 1D CNN. A smart data
balancing technique has been employed for giving data hungry convolutional
neural network the advantage of a greater number of effective samples to learn
from. For each coarse class, a separate Stochastic Gradient Descent (SGD) based
classifier has been used in order to differentiate among the finer classes
within that coarse class. TF-IDF representation of each word has been used as
feature for the SGD classifiers implemented as part of second stage
classification. Experiments show the effectiveness of our proposed method for
Bengali question classification.
