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Recently machine learning is being applied to almost every data domain one ofwhich is Question Answering Systems (QAS). A typical Question Answering Systemis fairly an information retrieval system, which matches documents or text andretrieve the most accurate one. The idea of open domain question answeringsystem put forth, involves convolutional neural network text classifiers. TheClassification model presented in this paper is multi-class text classifier.The neural network classifier can be trained on large dataset. We report seriesof experiments conducted on Convolution Neural Network (CNN) by training it ontwo different datasets. Neural network model is trained on top of wordembedding. Softmax layer is applied to calculate loss and mapping ofsemantically related words. Gathered results can help justify the fact thatproposed hypothetical QAS is feasible. We further propose a method to integrateConvolutional Neural Network Classifier to an open domain question answeringsystem. The idea of Open domain will be further explained, but the generalityof it indicates to the system of domain specific trainable models, thus makingit an open domain.

Convolutional Neural Network: Text Classification Model for Open Domain Question Answering System