In this section, we will model the patterns of letters that form English words. Beyond having different words, and sometimes alphabets, languages differ from each other in the patterns of letters that are used to form words. English words have a characteristic distribution of letters and letter sequences, and in this section we will try to model the process of word formation in a very simplistic way by using a hidden Markov model.
The emitted symbols of our model will be the letters themselves, but this time we don't know what the states could be as we are using unlabeled data. For this reason, we are going to provide just the number of states that we want our model to have, and then use the Baum-Welch algorithm to train the parameters of our HMM.
All we need for this task is a corpus of text in English. Earlier in this chapter, we studied movie reviews with the Naïve Bayes classifier, so we will use these for convenience, although other sources...
