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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.
Understanding why such words are tagged as they are in each context can help us clarify the distinctions between the tags.
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.
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.