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Markov Models

Markov Models. notes for CSCI-GA.2590 Prof. Grishman. Markov Model . In principle each decision could depend on all the decisions which came before (the tags on all preceding words in the sentence)

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Markov Models

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  1. Markov Models notes for CSCI-GA.2590 Prof. Grishman

  2. Markov Model • In principle each decision could depend on all the decisions which came before (the tags on all preceding words in the sentence) • But we’ll make life simple by assuming that the decision depends on only the immediately preceding decision • [first-order] Markov Model • representable by a finite state transition network • Tij = probability of a transition from state i to state j

  3. Finite State Network 0.30 dog: woof 0.50 0.30 start end 0.40 0.40 cat: meow 0.50 0.30 0.30

  4. Our bilingual pets • Suppose our cat learned to say “woof” and our dog “meow” • … they started chatting in the next room • … and we wanted to know who said what

  5. Hidden State Network woof meow woof meow dog start end cat

  6. How do we predict • When the cat is talking: ti = cat • When the dog is talking: ti = dog • We construct a probabilistic model of the phenomenon • And then seek the most likely state sequence S

  7. Hidden Markov Model • Assume current word depends only on current tag

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