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Categorizing and Tagging Words 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. What are lexical categories and how are they used in natural language processing? What is a good Python data structure for storing words and their categories? How can we automatically tag each word of a text with its word class? Along the way, we’ll cover some fundamental techniques in NLP, including sequence labeling, n-gram models, backoff, and evaluation.

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. Our emphasis in this chapter is on exploiting tags, and tagging text automatically. NLTK provides documentation for each tag, which can be queried using the tag, e. Some corpora have README files with tagset documentation, see nltk. Thus, we need to know which word is being used in order to pronounce the text correctly.

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