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in this video ill introduce conditional random fields ill talk about how they fit in among the other classification models weve talked about and then provide some intuition behind their design after that ill walk through the equations that they use for classification and parameter training so far weve learned about a variety of different classification techniques we started out by learning about hmms which were an extension of finite state automata then we moved on to naive bayes and then in this module we focused on logistic regression conditional random fields are another common classification technique in natural language processing when youre categorizing different types of classification techniques there are several important distinctions that can be made between approaches for example theres the type of label theyre designed to provide so naive bayes and logistic regression were built to provide a single label for a cohesive text instance whereas hmms and crfs were built