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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.

A word frequency table allows us to look up a word and find its frequency in a text collection.

In all these cases, we are mapping from names to numbers, rather than the other way around as with a list.

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We will also see how tagging is the second step in the typical NLP pipeline, following tokenization.

Notice that they are not in the same order they were originally entered; this is because dictionaries are not sequences but mappings (cf. Alternatively, to just find the keys, we can convert the dictionary to a list If we try to access a key that is not in a dictionary, we get an error.

However, its often useful if a dictionary can automatically create an entry for this new key and give it a default value, such as zero or the empty list.

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.

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