nltk.tag.HiddenMarkovModelTrainer.train_supervised

HiddenMarkovModelTrainer.train_supervised(labelled_sequences, estimator=None)[source]

Supervised training maximising the joint probability of the symbol and state sequences. This is done via collecting frequencies of transitions between states, symbol observations while within each state and which states start a sentence. These frequency distributions are then normalised into probability estimates, which can be smoothed if desired.

Returns:

the trained model
Return type:

HiddenMarkovModelTagger
Parameters:
  • labelled_sequences (list) – the training data, a set of labelled sequences of observations
  • estimator – a function taking a FreqDist and a number of bins and returning a CProbDistI; otherwise a MLE estimate is used