Given a list below or another list, where you have a word and its gramatic tag. Split the dataset into a training set and a test set. Create a Hidden Markov Model, where we use the smoothing technique. Within that model, we use the best$\_$path function within the model to predict the tags for the test set. That is taking the test set and removing the tags for each sentence to run through the best$\_$path function to predict those tags.

list $=[[("$the$",$ "det"$),("$further$",$ "adv"$),("$which$",$ "det"$),("$constructed$",$ "verb"$),("$report$",$ "noun"$)],$

$[("$been$",$ "verb"$),("$judge$",$ "noun"$),("$pastor$",$ "noun"$),("$the$",$ "det"$),("$in$",$ "adj"$),("$was$",$ "verb"$)],$

$[("$that$",$ "adp"$),("$to$","$prt$"),("$and$",$ "conj"$),("$johnson$",$ "noun"$),("$it$",$ "pron"$),("$jury$",$ "noun"$)]]$