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. AMarkov chainis a modelMarkov chainthat tells us something about the probabilities of sequences of random variables,states, each of which can take on values from some set.
2 APPENDIX A•HIDDEN MARKOV MODELS state must sum to 1. FigureA.1b shows a Markov chain for assigning a probabil-ity to a sequence of words w 1:::w n. This Markov chain should be familiar; in fact, it represents a bigram language model, with each edge expressing the probability p(w ijw j)! Given the two models in Fig.A.1, we can assign a ...
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