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In this video tutorial, the focus is on conditional random fields (CRFs) as a more general form of hidden Markov models (HMMs). The story begins with a discussion of HMMs, highlighting how a CRF can be seen as a broader version of an HMM. The video emphasizes the need to understand the rationale behind transitioning from a specific model like an HMM to a more complex model like a CRF, encouraging viewers to question the purpose and benefits of adding additional features to the model.
