The Viterbi algorithm is a common algorithm for parsing parts of speech given you have a matrix of transition states and emission states (see transitions.csv and emissions.csv).
The transition matrix defines the probability values for transitioning from one part of speech to another. The column is the t value, the row is the t-1 value.
In this example:
| Abbreviation | Part of Speech |
|---|---|
| DT | Determiner |
| RB | Adverb |
| NN | Noun |
| JJ | Adjective |
| VB | Verb |
| MD | Modal |
| NNP | Noun Phrase |
How to run:
model = Viterbi()
path, prob = model.run('Janet will back the bill')
Output:
Max Sequence: ['NNP', 'MD', 'VB', 'DT', 'NN']
Seq Probability: 2.013570710221386e-15