Transcription of CHAPTER A - Stanford University
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CHAPTER A - Stanford University
Speech and Language Processing. Daniel Jurafsky & James H. Martin. Copyright 2021. Allrights reserved. Draft of December 29, Markov ModelsChapter 8 introduced the Hidden Markov Model and applied it to part of speechtagging. Part of speech tagging is a fully-supervised learning task, because we havea corpus of words labeled with the correct part-of-speech tag. But many applicationsdon t have labeled data. So in this CHAPTER , we introduce the full set of algorithms forHMMs, including the key unsupervised learning algorithm for HMM, the Forward-Backward algorithm. We ll repeat some of the text from CHAPTER 8 for readers whowant the whole story laid out in a single Markov ChainsThe HMM is based on augmenting the Markov chain.
A.2•THE HIDDEN MARKOV MODEL 3 First, as with a first-order Markov chain, the probability of a particular state depends only on the previous state: Markov Assumption: P( q i j 1::: i 1)= i i 1) (A.4) Second, the probability of an output observation o i depends only on the state that produced the observation q i and not on any other states or ...
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