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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.
A word frequency table allows us to look up a word and find its frequency in a text collection.
method that divides up the tagged words into sentences rather than presenting them as one big list.
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.
is an association between a word and a part-of-speech tag.
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.
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.
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.
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.