He ' s literally the last villain we have after Bandos died , and Zamorak turned out to be misunderstood , while I agree he deserves to die , killing him off would be so cliche and boring . <| sep |> If you want a very simple model makes a sentence based on part of speech , the fastest that doesn ' t really use any ML techniques would be to create a database of words for each part of speech , then replacing each word in any quote with a random word of the same part of speech e . g . given the quote Peter drank coffee subject pastverb object Dog processed lamp , so each word matches the part of speech in the original quote . A better , relatively simple method is using Markov chains , which work by basically constructing a posterior distribution of the next word in a sentence given the current word . They work surprisingly well for a simple model simple in that the prediction is not conditioned on any other past words that have been created so far . These are fairly common some nice guides are andyou and gramsandmarkovchains . That method doesn ' t really use any concepts of part of speech , just Ngrams . With some NLP tinkering , you could improve this Markov chain method by conditioning the posterior distribution on not only the current word but also the part of speech , so the prediction takes into account some grammar as well as vocabulary . It would look more authentic , but not sure by how much . The easiest improvement would be to process much more text in training before trying this . Among the best methods out there in text generation is using RNNsLSTMs Recurrent Neural Networks , Long Short Term Memory , which might be what you were referring to . Here ' seffectiveness a famous example of someone using one to generate Shakespeare , Wikipedia text , and even code that look extremely realistic , even if the content is rubbish . However , it doesn ' t use any NLP it models the posterior distribution of the next letter given some past sequence of letters it has already predicted . Whatever method you use , be sure to use a large amount of text representative of what you want to generate so collecting maybe a few hundred megabytes of quotes that look like what you want to generate , not random wiki articles or instruction manuals for instance , in a text file , and using that as your training set .