Natural language processing (NLP) is a theory-motivated range of computational techniques for the automatic analysis and representation of human language. NLP research has evolved from the era of punch cards and batch processing (in which the analysis of a sentence could take up to 7 minutes) to the era of Google and the likes of it (in which millions of webpages can be processed in less than a second). NLP is one of the dominant fields in data mining. With the increasing importance of Big Data Analytics today, NLP plays a major role in acquiring relevant information of importance to business and intelligence. Millions of items are uploaded on Web every day, with relevant as well as irrelevant data. Information retrieval and extraction from reviews, comments, social media etc. by customers is a complex task since most of the information is in semi structured and unstructured form. Ambiguity of large corpora on Web underlines the need for decent and efficient data There are no sources in the current document.a mining technique. Due to the recent progress in Deep Neural Networks, Reinforcement Learning (RL) has become one of the most important and useful technology. It is a learning method where a software agent interacts with an unknown environment, selects actions, and progressively discovers the environment dynamics. RL has been effectively applied in many important areas of real life. 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. Many Natural Language Processing (NLP) tasks (including generation, language grounding, reasoning, information extraction, coreference resolution, and dialog) can be formulated as deep reinforcement learning (DRL) problems which are explained as the different models in this paper

Natural Language Processing, Neural Networks, Deep Learning, Reinforcement Learning