Lecture 7: Hidden Markov Models (HMMs)

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This document is a lecture on hidden Markov models (HMMs) given by Marina Santini at Uppsala University. The lecture covers the basics of HMMs, including Markov assumptions, observation sequences, problems with HMMs, the Viterbi, forward, and backward algorithms, modeling for part-of-speech tagging, learning, smoothing, and inference in HMMs, as well as applications of HMMs. The lecture acknowledges Joakim Nivre for course design and materials.

Education

Machine Learning for Language Technology
Lecture 7: Hidden Markov Models (HMMs)
Marina Santini
Department of Linguistics and Philology
Uppsala University, Uppsala, Sweden
Autumn 2014
Acknowledgement: Thanks to Prof. Joakim Nivre for course design and materials

In this presentation we describe the formulation of the HMM model as consisting of states that are hidden that generate the observables. We introduce the 3 basic problems: Finding the probability of a sequence of observation given the model, the decoding problem of finding the hidden states given the observations and the model and the training problem of determining the model parameters that generate the given observations. We discuss the Forward, Backward, Viterbi and Forward-Backward algorithms.

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