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[Music] in this segment were going to talk about conditional random fields this is going to be a model alternative to hidden markov models that is still going to allow us to incorporate sequential structure but is now going to allow us to do it in the context of a discriminative model but first lets talk about named entity recognition which were going to use as our motivating example here so named entity recognition is roughly the following problem if we have a sentence like barack obama will travel to hangzhou today for the g20 meeting we want to identify that there are some chunks of uh essentially sequences of words in this sentence that correspond to real world entities and we want to label those entities with what type they are for example barack obama is a person hong-joo is a location and the g20 is an organization the way were going to do this is were going to treat it as a tagging problem so we can do this by encoding these chunks in with whats called a bio tag site and
