Once we start doing part-of-speech tagging, we will be creating programs that assign a tag to a word, the tag which is most likely in a given context.
Consider the following analysis involving By convention in NLTK, a tagged token is represented using a tuple consisting of the token and the tag.
We can create one of these special tuples from the standard string representation of a tagged token, using the function Other corpora use a variety of formats for storing part-of-speech tags.
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.
In this section, we will see how to represent such mappings in Python.
data type that can be used for mapping between arbitrary types.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.Back in elementary school you learnt the difference between nouns, verbs, adjectives, and adverbs.These "word classes" are not just the idle invention of grammarians, but are useful categories for many language processing tasks.Since words and tags are paired, we can treat the word as a condition and the tag as an event, and initialize a conditional frequency distribution with a list of condition-event pairs.