Package cc.mallet.fst

Transducers, including Conditional Random Fields (CRFs).

See:
Description

Interface Summary

CacheStaleIndicator	Indicates when the value/gradient during training becomes stale.
MaxLattice	The interface to classes implementing the Viterbi algorithm, finding the best sequence of states for a given input sequence.
SumLattice	Interface to perform forward-backward during training of a transducer.
Transducer.Incrementor	Methods to be called by inference methods to indicate partial counts of sufficient statistics.
TransducerTrainer.ByOptimization

Class Summary

CRF	Represents a CRF model.
CRF.Factors	A simple, transparent container to hold the parameters or sufficient statistics for the CRF.
CRF.State
CRF.TransitionIterator
CRFCacheStaleIndicator	Indicates when the value/gradient becomes stale based on updates to CRF's parameters.
CRFOptimizableByBatchLabelLikelihood	Implements label likelihood gradient computations for batches of data, can be easily parallelized.
CRFOptimizableByBatchLabelLikelihood.Factory
CRFOptimizableByGradientValues	A CRF objective function that is the sum of multiple objective functions that implement Optimizable.ByGradientValue.
CRFOptimizableByLabelLikelihood	An objective function for CRFs that is the label likelihood plus a Gaussian or hyperbolic prior on parameters.
CRFOptimizableByLabelLikelihood.Factory
CRFTrainerByL1LabelLikelihood	CRF trainer that implements L1-regularization.
CRFTrainerByLabelLikelihood	Unlike ClassifierTrainer, TransducerTrainer is not "stateless" between calls to train.
CRFTrainerByStochasticGradient	Trains CRF by stochastic gradient.
CRFTrainerByThreadedLabelLikelihood
CRFTrainerByValueGradients	A CRF trainer that can combine multiple objective functions, each represented by a Optmizable.ByValueGradient.
CRFWriter	Saves a trained model to specified filename.
FeatureTransducer
HMM	A Hidden Markov Model.
HMM.State
HMM.TransitionIterator
HMMTrainerByLikelihood
InstanceAccuracyEvaluator	Reports the percentage of instances for which the entire predicted sequence was correct.
LabelDistributionEvaluator	Prints predicted and true label distribution.
MaxLatticeDefault	Default, full dynamic programming version of the Viterbi "Max-(Product)-Lattice" algorithm.
MaxLatticeDefault.Factory
MaxLatticeFactory
MEMM	A Maximum Entropy Markov Model.
MEMM.State
MEMM.TransitionIterator
MEMMTrainer	Trains and evaluates a MEMM.
MultiSegmentationEvaluator	Evaluates a transducer model, computes the precision, recall and F1 scores; considers segments that span across multiple tokens.
NoopTransducerTrainer	A TransducerTrainer that does no training, but simply acts as a container for a Transducer; for use in situations that require a TransducerTrainer, such as the TransducerEvaluator methods.
PerClassAccuracyEvaluator	Determines the precision, recall and F1 on a per-class basis.
Segment	Represents a labelled chunk of a Sequence segmented by a Transducer, usually corresponding to some object extracted from an input Sequence.
SegmentationEvaluator
ShallowTransducerTrainer	Deprecated. Use NoopTransducerTrainer instead
SimpleTagger	This class's main method trains, tests, or runs a generic CRF-based sequence tagger.
SimpleTagger.SimpleTaggerSentence2FeatureVectorSequence	Converts an external encoding of a sequence of elements with binary features to a FeatureVectorSequence.
SumLatticeBeam
SumLatticeBeam.Factory
SumLatticeConstrained
SumLatticeDefault	Default, full dynamic programming implementation of the Forward-Backward "Sum-(Product)-Lattice" algorithm
SumLatticeDefault.Factory
SumLatticeFactory	Provides factory methods to create inference engine for training a transducer.
SumLatticeScaling
SumLatticeScaling.Factory
ThreadedOptimizable	An adaptor for optimizables based on batch values/gradients.
TokenAccuracyEvaluator	Evaluates a transducer model based on predictions of individual tokens.
Transducer	A base class for all sequence models, analogous to classify.Classifier.
Transducer.State	An abstract class used to represent the states of the transducer.
Transducer.TransitionIterator	An abstract class to iterate over the states of the transducer.
TransducerEvaluator	An abstract class to evaluate a transducer model.
TransducerTrainer	An abstract class to train and evaluate a transducer model.
TransducerTrainer.ByIncrements
TransducerTrainer.ByInstanceIncrements
ViterbiWriter	Prints the input instances along with the features and the true and predicted labels to a file.

Package cc.mallet.fst Description

Transducers, including Conditional Random Fields (CRFs).