Okay, here’s the problem we’re going to solve in this section. We have a sentence, for example, Mary had a little lamb, and we need to figure out which words are nouns, verbs, adjectives, adverbs, et cetera. Now, a thorough knowledge in grammar is not needed in this section only to know that words are normally given these labels or tags. For this sentence, the tags are noun for Mary, verb for had, determinant for a, adjective for little, and again noun for lamb. These labels are called parts of speech and so this problem is called part-of-speech tagging, and what we’ll do in this section is develop some models that will help us label these words with their corresponding part of speech based on existing label data. The main model that we’ll learn today is a hidden Markov model. So for simplicity, in this section we’ll only be using three parts of speech. Along in the project, you’ll use more. The ones we’ll use are noun. So, things like house, car, Mary, Bob, et cetera. Then, we have a modal verb, which is normally used with another verb. These are words like can, would, should, may, et cetera. And finally, we’ll have verbs, which are actions like run, see, jump, eat, walk, et cetera. So, when we have a sentence like, John can run, we tag it with the parts of speech as follows. John is the noun, can is the modal verb, and run is the verb.