Literature survey of statistical, deep and reinforcement learning in natural language processing

This paper underlines the necessity to incorporate Deep learning and Neural networking in language models under scrutiny for Natural Language Processing. The paper describes various statistical models proposed and the limitations incurred in the same due to limited intelligence of a machine. We have discussed different neural networks highlighting the importance of Convolutional Neural Networking. We have discussed about open source software TensorFlow that works on Deep learning and the edge it has over the conventional models. Also we have recommended Reinforcement learning as an extension to neural networking which is widely used in gaming for the purpose of Natural Language Processing. We can utilize the reward-driven algorithm for better results.