You might wonder what justification there is for introducing this extra level of information.
Many of these categories arise from superficial analysis the distribution of words in text.
Consider the following analysis involving By convention in NLTK, a tagged token is represented using a tuple consisting of the token and the tag.
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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.
The process of classifying words into their is a noun meaning "trash" (i.e. Thus, we need to know which word is being used in order to pronounce the text correctly.
(For this reason, text-to-speech systems usually perform POS-tagging.) seem to have their uses, but the details will be obscure to many readers.
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 extract shown above, the corpus reader for the Brown Corpus represents the data as shown below.Note that part-of-speech tags have been converted to uppercase, since this has become standard practice since the Brown Corpus was published.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.As we will see, they arise from simple analysis of the distribution of words in text.The goal of this chapter is to answer the following questions: Along the way, we'll cover some fundamental techniques in NLP, including sequence labeling, n-gram models, backoff, and evaluation.