A language model can predict the probability of the next word in the sequence, based on the words already observed in the sequence. Neural network models are a preferred method for developing statistical language models because they can use a distributed representation where different words with similar meanings have similar representation and because they can use a large context of recently observed words when making predictions. In this tutorial, you will discover how to develop a statistical language model using deep learning in Python. How to Develop a Word-Level Neural Language Model and Use it to Generate Text Photo by Carlo Raso, some rights reserved. The Republic is the classical Greek philosopher Plato's most famous work. It is structured as a dialog (e.g. The entire text is available for free in the public domain. It is available on the Project Gutenberg website in a number of formats.