Part-of-speech tagging is the process of assigning a part-of-speech to each word in a text. The input is a sequence x 1 , x 2 , ..., x n of (tokenized) words and a tagset, and the output is a sequence y 1 , y 2 , ..., y n of tags, each output y i corresponding exactly to one input x i , as shown in the intuition in Fig. 8.3 . Tagging is a disambiguation task; words are ambiguous -have more than one ambiguous possible part-of-speech-and the goal is to find the correct tag for the situation. For example, book can be a verb (book that flight) or a noun (hand me that book). That can be a determiner (Does that flight serve dinner) or a complementizer (I We'll introduce algorithms for the task in the next few sections, but first let's explore the task. Exactly how hard is it? Fig. 8.4 shows that most word types (85-86%) are unambiguous (Janet is always NNP, hesitantly is always RB). But the ambiguous words, though accounting for only 14-15% of the vocabulary, are very common, and 55-67% of word tokens in running text are ambiguous. Particularly ambiguous common words include that, back, down, put and set; here are some examples of the 6 different parts of speech for the word back: earnings growth took a back/JJ seat a small building in the back/NN a clear majority of senators back/VBP the bill Dave began to back/VB toward the door enable the country to buy back/RP debt I was twenty-one back/RB then Nonetheless, many words are easy to disambiguate, because their different tags aren't equally likely. For example, a can be a determiner or the letter a, but the determiner sense is much more likely.