edu.stanford.nlp.parser.shiftreduce

Class ShiftReduceTrainOptions

java.lang.Object

edu.stanford.nlp.parser.lexparser.TrainOptions

edu.stanford.nlp.parser.shiftreduce.ShiftReduceTrainOptions

All Implemented Interfaces:

Serializable

public class ShiftReduceTrainOptions
extends TrainOptions

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	ShiftReduceTrainOptions.TrainingMethod

Nested classes/interfaces inherited from class edu.stanford.nlp.parser.lexparser.TrainOptions

TrainOptions.TransformMatrixType

Field Summary

Fields

Modifier and Type	Field and Description
int	averagedModels If set to 0, training outputs the last model produced, regardless of its score.
int	beamSize
boolean	cvAveragedModels Cross-validate over the number of models to average, using the dev set, to figure out which number between 1 and averagedModels we actually want to use
static int	DEFAULT_BEAM_SIZE
int	featureFrequencyCutoff How many times a feature must be seen when training.
boolean	oracleBinaryToShift Does help, but makes the models much bigger for a miniscule gain
boolean	oracleShiftToBinary Does not seem to help...
boolean	retrainAfterCutoff If we cut off features with featureFrequencyCutoff, this retrains with only the existing features
boolean	saveIntermediateModels Saves intermediate models, but that takes up a lot of space
ShiftReduceTrainOptions.TrainingMethod	trainingMethod

Fields inherited from class edu.stanford.nlp.parser.lexparser.TrainOptions

basicCategoryTagsInDependencyGrammar, batchSize, cheatPCFG, collinsPunc, compactGrammar, debugOutputFrequency, DEFAULT_BATCH_SIZE, DEFAULT_DELTA_MARGIN, DEFAULT_K_BEST, DEFAULT_LEARNING_RATE, DEFAULT_QN_ITERATIONS_PER_BATCH, DEFAULT_REGCOST, DEFAULT_SCALING_FOR_INIT, DEFAULT_STALLED_ITERATION_LIMIT, DEFAULT_TRAINING_ITERATIONS, DEFAULT_UNK_WORD, deleteSplitters, deltaMargin, dvKBest, dvSimplifiedModel, fractionBeforeUnseenCounting, gPA, HSEL_CUT, hSelSplit, learningRate, leftRec, leftToRight, lowercaseWordVectors, markFinalStates, markovFactor, markovOrder, markStrahler, markUnary, markUnaryTags, maxTrainTimeSeconds, noRebinarization, noTagSplit, openClassTypesThreshold, PA, postGPA, postPA, postSplitters, postSplitWithBaseCategory, predictSplits, preTransformer, printAnnotatedPW, printAnnotatedRuleCounts, printAnnotatedStateCounts, printBinarizedPW, printStates, printTreeTransformations, qnEstimates, qnIterationsPerBatch, qnTolerance, randomSeed, regCost, rightRec, ruleDiscount, ruleSmoothing, ruleSmoothingAlpha, scalingForInit, selectivePostSplit, selectivePostSplitCutOff, selectiveSplit, selectiveSplitCutOff, simpleBinarizedLabels, sisterAnnotate, sisterSplitters, smoothing, splitCount, splitPrePreT, splitRecombineRate, splitters, stalledIterationLimit, taggedFiles, tagPA, tagSelectivePostSplit, tagSelectivePostSplitCutOff, tagSelectiveSplit, tagSelectiveSplitCutOff, trainingIterations, trainingThreads, trainLengthLimit, trainTreeFile, trainWordVectors, transformMatrixType, unknownCapsVector, unknownChineseNumberVector, unknownChinesePercentVector, unknownChineseYearVector, unknownDashedWordVectors, unknownNumberVector, unkWord, useContextWords

Constructor Summary

Constructors

Constructor and Description
ShiftReduceTrainOptions()

Method Summary

Methods inherited from class edu.stanford.nlp.parser.lexparser.TrainOptions

compactGrammar, display, outsideFactor, printTrainTree, toString

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Field Detail

averagedModels

public int averagedModels

If set to 0, training outputs the last model produced, regardless of its score. Otherwise it takes the best k models and averages them together.

cvAveragedModels

public boolean cvAveragedModels

Cross-validate over the number of models to average, using the dev set, to figure out which number between 1 and averagedModels we actually want to use

trainingMethod

public ShiftReduceTrainOptions.TrainingMethod trainingMethod

DEFAULT_BEAM_SIZE

public static final int DEFAULT_BEAM_SIZE

See Also:

Constant Field Values

beamSize

public int beamSize

featureFrequencyCutoff

public int featureFrequencyCutoff

How many times a feature must be seen when training. Less than this and it is filtered.

saveIntermediateModels

public boolean saveIntermediateModels

Saves intermediate models, but that takes up a lot of space

retrainAfterCutoff

public boolean retrainAfterCutoff

If we cut off features with featureFrequencyCutoff, this retrains with only the existing features

oracleShiftToBinary

public boolean oracleShiftToBinary

Does not seem to help... perhaps there is a logic bug in how to compensate for missed binary transitions

oracleBinaryToShift

public boolean oracleBinaryToShift

Does help, but makes the models much bigger for a miniscule gain

Constructor Detail

ShiftReduceTrainOptions

public ShiftReduceTrainOptions()