= Daniel Jurafsky
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<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.<Speech and Language Processing by Daniel Jurafsky and James H. Martin>[AppendixA, Hidden Markov Models]{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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