  Convolutional neural networks for computer vision are fairly intuitive. In a
typical CNN used in image classification, the first layers learn edges, and the
following layers learn some filters that can identify an object. But CNNs for
Natural Language Processing are not used often and are not completely
intuitive. We have a good idea about what the convolution filters learn for the
task of text classification, and to that, we propose a neural network structure
that will be able to give good results in less time. We will be using
convolutional neural networks to predict the primary or broader topic of a
question, and then use separate networks for each of these predicted topics to
accurately classify their sub-topics.
