Hidden Markov Model

Also known as: HMM

A Hidden Markov Model is a probabilistic model of sequences where the system transitions between hidden states, each of which emits observable symbols according to learned probability distributions. In NLP, HMMs were the dominant approach for part-of-speech tagging and speech recognition before neural methods: hidden states represent grammatical tags or phonemes, and observations represent words or acoustic features. The Viterbi algorithm efficiently finds the most probable hidden state sequence given an observed sequence.