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Packages that use CRF | |
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cc.mallet.extract | Unimplemented. |
cc.mallet.fst | Transducers, including Conditional Random Fields (CRFs). |
Uses of CRF in cc.mallet.extract |
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Methods in cc.mallet.extract that return CRF | |
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CRF |
CRFExtractor.getCrf()
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Constructors in cc.mallet.extract with parameters of type CRF | |
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CRFExtractor(CRF crf)
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CRFExtractor(CRF crf,
Pipe tokpipe)
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CRFExtractor(CRF crf,
Pipe tokpipe,
TokenizationFilter filter)
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CRFExtractor(CRF crf,
Pipe tokpipe,
TokenizationFilter filter,
java.lang.String backgroundTag)
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Uses of CRF in cc.mallet.fst |
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Subclasses of CRF in cc.mallet.fst | |
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class |
MEMM
A Maximum Entropy Markov Model. |
Fields in cc.mallet.fst declared as CRF | |
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protected CRF |
CRFTrainerByStochasticGradient.crf
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protected CRF |
CRFOptimizableByLabelLikelihood.crf
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protected CRF |
CRFOptimizableByBatchLabelLikelihood.crf
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protected CRF |
CRFCacheStaleIndicator.crf
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Methods in cc.mallet.fst that return CRF | |
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CRF |
CRFTrainerByValueGradients.getCRF()
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CRF |
CRFTrainerByLabelLikelihood.getCRF()
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static CRF |
SimpleTagger.train(InstanceList training,
InstanceList testing,
TransducerEvaluator eval,
int[] orders,
java.lang.String defaultLabel,
java.lang.String forbidden,
java.lang.String allowed,
boolean connected,
int iterations,
double var,
CRF crf)
Create and train a CRF model from the given training data, optionally testing it on the given test data. |
Methods in cc.mallet.fst with parameters of type CRF | |
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Optimizable.ByGradientValue |
CRFOptimizableByLabelLikelihood.Factory.newCRFOptimizable(CRF crf,
InstanceList trainingData)
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Optimizable.ByCombiningBatchGradient |
CRFOptimizableByBatchLabelLikelihood.Factory.newCRFOptimizable(CRF crf,
InstanceList trainingData,
int numBatches)
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protected CRF.State |
MEMM.newState(java.lang.String name,
int index,
double initialWeight,
double finalWeight,
java.lang.String[] destinationNames,
java.lang.String[] labelNames,
java.lang.String[][] weightNames,
CRF crf)
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protected CRF.State |
CRF.newState(java.lang.String name,
int index,
double initialWeight,
double finalWeight,
java.lang.String[] destinationNames,
java.lang.String[] labelNames,
java.lang.String[][] weightNames,
CRF crf)
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static CRF |
SimpleTagger.train(InstanceList training,
InstanceList testing,
TransducerEvaluator eval,
int[] orders,
java.lang.String defaultLabel,
java.lang.String forbidden,
java.lang.String allowed,
boolean connected,
int iterations,
double var,
CRF crf)
Create and train a CRF model from the given training data, optionally testing it on the given test data. |
Constructors in cc.mallet.fst with parameters of type CRF | |
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CRF.Factors(CRF crf)
Construct a new Factors with the same structure as the parameters of 'crf', but with values initialized to zero. |
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CRF.State(java.lang.String name,
int index,
double initialWeight,
double finalWeight,
java.lang.String[] destinationNames,
java.lang.String[] labelNames,
java.lang.String[][] weightNames,
CRF crf)
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CRF.TransitionIterator(CRF.State source,
FeatureVectorSequence inputSeq,
int inputPosition,
java.lang.String output,
CRF crf)
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CRF.TransitionIterator(CRF.State source,
FeatureVector fv,
java.lang.String output,
CRF crf)
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CRF(CRF other)
Create a CRF whose states and weights are a copy of those from another CRF. |
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CRFCacheStaleIndicator(CRF crf)
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CRFOptimizableByBatchLabelLikelihood(CRF crf,
InstanceList ilist,
int numBatches)
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CRFOptimizableByLabelLikelihood(CRF crf,
InstanceList ilist)
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CRFTrainerByL1LabelLikelihood(CRF crf)
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CRFTrainerByL1LabelLikelihood(CRF crf,
double l1Weight)
Constructor for CRF trainer. |
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CRFTrainerByLabelLikelihood(CRF crf)
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CRFTrainerByStochasticGradient(CRF crf,
double learningRate)
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CRFTrainerByStochasticGradient(CRF crf,
InstanceList trainingSample)
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CRFTrainerByValueGradients.OptimizableCRF(CRF crf,
InstanceList ilist)
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CRFTrainerByValueGradients(CRF crf,
Optimizable.ByGradientValue[] optimizableByValueGradientObjects)
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MEMM.State(java.lang.String name,
int index,
double initialCost,
double finalCost,
java.lang.String[] destinationNames,
java.lang.String[] labelNames,
java.lang.String[][] weightNames,
CRF crf)
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MEMM.TransitionIterator(MEMM.State source,
FeatureVectorSequence inputSeq,
int inputPosition,
java.lang.String output,
CRF memm)
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MEMM.TransitionIterator(MEMM.State source,
FeatureVector fv,
java.lang.String output,
CRF memm)
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MEMM(CRF crf)
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