1. A method for performing natural language processing, comprising: receiving a primary text file; scanning the received primary text file to determine a set of statistics related to a frequency at which various words of the primary text file follow other words of the primary text file; creating a probabilistic word generator, based on the determined set of statistics, that generates synthetic text exhibiting the determined set of statistics; generating synthetic text directly into a main memory of a computer system, the generated synthetic text including a plurality of sentences, each of which including a predetermined number of probabilistically selected words exhibiting the determined set of statistics, related to a frequency at which various words of the primary text file follow other words of the primary text file, using the created probabilistic word generator; performing word vectorization on the synthetic text, within the main memory of the computer system; using results of the performed vectorization to perform machine learning tasks; and using the machine learning tasks to perform natural language processing to interpret a subsequent text, wherein, in generating the synthetic text that is used to perform machine learning directly into the main memory, no text data is loaded into the main memory from a memory storage device.