Viterbi Algorithm Ralph Grishman G22.2590 - Natural Language Processing.

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Presentation transcript:

Viterbi Algorithm Ralph Grishman G Natural Language Processing

Computing Probabilities viterbi [ s, t ] = max(s’) ( viterbi [ s’, t-1] * transition probability P(s | s’) * emission probability P (token[t] | s) ) for each s, t: record which s’, t-1 contributed the maximum

Analyzing Fish sleep.

A Simple POS HMM startnounverb end

Word Emission Probabilities P ( word | state ) A two-word language: “fish” and “sleep” Suppose in our training corpus, “fish” appears 8 times as a noun and 4 times as a verb “sleep” appears twice as a noun and 6 times as a verb Emission probabilities: Noun –P(fish | noun) :0.8 –P(sleep | noun) :0.2 Verb –P(fish | verb) :0.4 –P(sleep | verb) :0.6

Viterbi Probabilities

startnounverb end

startnounverb end Token 1: fish

startnounverb end Token 1: fish

startnounverb end Token 2: sleep (if ‘fish’ is verb)

startnounverb end Token 2: sleep (if ‘fish’ is verb)

startnounverb end Token 2: sleep (if ‘fish’ is a noun)

startnounverb end Token 2: sleep (if ‘fish’ is a noun)

startnounverb end Token 2: sleep take maximum, set back pointers

startnounverb end Token 2: sleep take maximum, set back pointers

startnounverb end Token 3: end

startnounverb end Token 3: end take maximum, set back pointers

startnounverb end Decode: fish = noun sleep = verb