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These techniques are useful in many areas, and tagging gives us a simple context in which to present them.
We will also see how tagging is the second step in the typical NLP pipeline, following tokenization.
This will be useful when we come to developing automatic taggers, as they are trained and tested on lists of sentences, not words. Let's inspect some tagged text to see what parts of speech occur before a noun, with the most frequent ones first.
To begin with, we construct a list of bigrams whose members are themselves word-tag pairs such as Note that the items being counted in the frequency distribution are word-tag pairs.
3.1 lists a variety of linguistic objects, along with what they map.
As we will see, they arise from simple analysis of the distribution of words in text.