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NLTK's corpus readers provide a uniform interface so that you don't have to be concerned with the different file formats.In contrast with the file fragment shown above, the corpus reader for the Brown Corpus represents the data as shown below.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.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.
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.