nltk.tag.HiddenMarkovModelTrainer¶
- class nltk.tag.HiddenMarkovModelTrainer[source]¶
Bases:
objectAlgorithms for learning HMM parameters from training data. These include both supervised learning (MLE) and unsupervised learning (Baum-Welch).
Creates an HMM trainer to induce an HMM with the given states and output symbol alphabet. A supervised and unsupervised training method may be used. If either of the states or symbols are not given, these may be derived from supervised training.
- Parameters
states (sequence of any) – the set of state labels
symbols (sequence of any) – the set of observation symbols
- train(labeled_sequences=None, unlabeled_sequences=None, **kwargs)[source]¶
Trains the HMM using both (or either of) supervised and unsupervised techniques.
- Returns
the trained model
- Return type
- Parameters
labelled_sequences (list) – the supervised training data, a set of labelled sequences of observations ex: [ (word_1, tag_1),…,(word_n,tag_n) ]
unlabeled_sequences (list) – the unsupervised training data, a set of sequences of observations ex: [ word_1, …, word_n ]
kwargs – additional arguments to pass to the training methods
- train_unsupervised(unlabeled_sequences, update_outputs=True, **kwargs)[source]¶
Trains the HMM using the Baum-Welch algorithm to maximise the probability of the data sequence. This is a variant of the EM algorithm, and is unsupervised in that it doesn’t need the state sequences for the symbols. The code is based on ‘A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition’, Lawrence Rabiner, IEEE, 1989.
- Returns
the trained model
- Return type
- Parameters
unlabeled_sequences (list) – the training data, a set of sequences of observations
kwargs may include following parameters:
- Parameters
model – a HiddenMarkovModelTagger instance used to begin the Baum-Welch algorithm
max_iterations – the maximum number of EM iterations
convergence_logprob – the maximum change in log probability to allow convergence
- 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
- 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