Daniel Jurafsky coauthored Speech and Language Processing by Daniel Jurafsky and James H. Martin. The text presents mathematical models and computational procedures for natural language processing, including sequence models that connect linguistic observations to latent states.[ref]
In a Hidden Markov model, the current hidden state determines the emission distribution and the next-state probabilities. This separation permits the forward algorithm to compute an observation sequence's likelihood by summing locally over preceding states. The assumptions make inference tractable, while their suitability for a particular language task remains a modelling question. A tractable calculation does not by itself establish that the model captures every dependency in real language.
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