Generative Model for NLP Applications based on Component Extraction

Abstract People all around the world speak so many different languages, but a Computer System or any other Computerized Machine only understands a single language i.e. binary language (1s and 0s) This system or a process that converts human language to computer understandable language is known as Natural Language Processing (NLP), though various diversified models have suggested so far, yet the need for a generative predictive model which can optimize depending upon the nature of problem being addressed is still an area of research under work. The paper presents a Generative Model for NLP Applications based on significant components extracted from Case Studies. The generative model is a single platform for diversified areas of NLP that can address specific problems relating to read text, hear speech, interpret it, measure sentiment and determine which parts are important. This is achieved by process of elimination once the relevant components are identified. Single platform provides same model generating and reproducing optimized solutions and addressing different issues.