  Recently machine learning is being applied to almost every data domain one of
which is Question Answering Systems (QAS). A typical Question Answering System
is fairly an information retrieval system, which matches documents or text and
retrieve the most accurate one. The idea of open domain question answering
system put forth, involves convolutional neural network text classifiers. The
Classification model presented in this paper is multi-class text classifier.
The neural network classifier can be trained on large dataset. We report series
of experiments conducted on Convolution Neural Network (CNN) by training it on
two different datasets. Neural network model is trained on top of word
embedding. Softmax layer is applied to calculate loss and mapping of
semantically related words. Gathered results can help justify the fact that
proposed hypothetical QAS is feasible. We further propose a method to integrate
Convolutional Neural Network Classifier to an open domain question answering
system. The idea of Open domain will be further explained, but the generality
of it indicates to the system of domain specific trainable models, thus making
it an open domain.
