Generating Text with Markov Chains

An idea is expressed using the words of a language. As ideas are not tangible, it is useful to look at text generation in order to gauge whether a machine can think on its own. The utility of text generation is currently limited to an auto-complete functionality, besides a few negative use cases that we will discuss later in this section. Text can be generated in many different ways, which we will explore using Markov chains. Whether this generated text can correspond to a coherent line of thought is something that we will address later in this section.

Markov Chains

A state space defines all possible states that can exist. A Markov chain consists of a state space and a specific type of successor function. For example, in the case of the simplified state space to describe the weather, the states could be Sunny, Cloudy, or Rainy. The successor function describes how a system in its current state can move to a different state or even continue...