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.
However, as we see from 3.1, it has a much wider range of uses. As we saw above (line ), this gives us the key-value pairs.
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.
We can think of this process as : Dictionary Look-up: we access the entry of a dictionary using a key such as someone's name, a web domain, or an English word; other names for dictionary are map, hashmap, hash, and associative array. When we type a domain name in a web browser, the computer looks this up to get back an IP address.
Understanding why such words are tagged as they are in each context can help us clarify the distinctions between the tags.
is an association between a word and a part-of-speech tag.