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Parsing Engine | |||||||||
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Packages that use Model | |
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danbikel.parser | Provides the core framework of this extensible statistical parsing engine. |
danbikel.parser.ms |
Default package for model structure classes (subclasses of ProbabilityStructure ). |
Uses of Model in danbikel.parser |
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Subclasses of Model in danbikel.parser | |
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class |
InterpolatedKnesserNeyModel
Implements a model that uses interpolated Knesser-Ney smoothing. |
class |
JointModel
Provides a mechanism for grouping related Model objects in order
to estimate the probability of some joint event. |
Fields in danbikel.parser declared as Model | |
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protected Model |
ModelCollection.gapModel
The model for generating gaps. |
protected Model |
Trainer.gapModel
The gap-generation model. |
protected Model |
ModelCollection.headModel
The model for generating a head nonterminal given its (lexicalized) parent. |
protected Model |
Trainer.headModel
The head-generation model. |
protected Model |
ModelCollection.leftSubcatModel
The model for generating subcats on the left side of the head child. |
protected Model |
Trainer.leftSubcatModel
The model for generating subcats that fall on the left side of head children. |
protected Model |
ModelCollection.lexPriorModel
The model for lexical priors. |
protected Model |
Trainer.lexPriorModel
The model for marginal probabilities of lexical elements (for the estimation of the joint event that is a fully lexicalized nonterminal). |
protected Model[] |
ModelCollection.modelArr
An array containing all Model objects contained by this
model collection, set up by ModelCollection.createModelArray() . |
protected Model |
ModelCollection.modNonterminalModel
The model for generating partially-lexicalized nonterminals that modify the head child. |
protected Model |
Trainer.modNonterminalModel
The modifying nonterminal–generation model. |
protected Model |
ModelCollection.modWordModel
The model for generating head words of lexicalized nonterminals that modify the head child. |
protected Model |
Trainer.modWordModel
The model that generates head words of modifying nonterminals. |
protected Model |
ModelCollection.nonterminalPriorModel
The model for nonoterminal priors. |
protected Model |
Trainer.nonterminalPriorModel
The model for conditional probabilities of nonterminals given the lexical components (for the estimation of the joint event that is a fully lexicalized nonterminal). |
protected Model[] |
JointModel.otherModels
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protected Model |
ModelCollection.rightSubcatModel
The model for generating subcats on the right side of the head child. |
protected Model |
Trainer.rightSubcatModel
The model for generating subcats that fall on the right side of head children. |
protected Model |
ModelCollection.topLexModel
The model for generating the head word and part of speech of observed root nonterminals given the hidden +TOP+ nonterminal. |
protected Model |
Trainer.topLexModel
The head-word generation model for heads of entire sentences. |
protected Model |
ModelCollection.topNonterminalModel
The model for generating observed root nonterminals given the hidden +TOP+ nonterminal. |
protected Model |
Trainer.topNonterminalModel
The head-generation model for heads whose parents are Training.topSym() . |
Methods in danbikel.parser that return Model | |
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Model |
ModelCollection.gapModel()
Returns the gap-generation model. |
Model |
JointModel.getModel(int idx)
Returns this or any of the internal Model instances used
to produce joint probability estimates. |
Model |
Model.getModel(int idx)
Returns this model object. |
Model |
ModelCollection.headModel()
Returns the head-generation model. |
Model |
ModelCollection.leftSubcatModel()
Returns the left subcat-generation model. |
Model |
ModelCollection.lexPriorModel()
Returns the model for marginal probabilities of lexical elements (for the estimation of the joint event that is a fully lexicalized nonterminal) |
Model |
ModelCollection.modNonterminalModel()
Returns the modifying nonterminal–generation model. |
Model |
ModelCollection.modWordModel()
Returns the model that generates head words of modifying nonterminals. |
Model |
ProbabilityStructure.newModel()
Returns a newly-constructed Model object for this
probability structure. |
Model |
ModelCollection.nonterminalPriorModel()
Returns the model for conditional probabilities of nonterminals given the lexical components (for the estimation of the joint event that is a fully lexicalized nonterminal) |
Model |
ModelCollection.rightSubcatModel()
Returns the right subcat-generation model. |
Model |
ModelCollection.topLexModel()
Returns the head-word generation model for heads of entire sentences. |
Model |
ModelCollection.topNonterminalModel()
Returns the head-generation model for heads whose parents are Training.topSym() . |
Methods in danbikel.parser with parameters of type Model | |
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static void |
AnalyzeDisns.computeEntropyAndJSStats(Model model,
CountsTable[] entropy,
BiCountsTable[] js)
A method invoked by Model when Settings.modelDoPruning is true: entropy values and
JS divergence values are used in the parameter-pruning method. |
static CountsTable[] |
AnalyzeDisns.computeModelEntropies(Model model)
A method to compute a model's entropy statistics for all estimated distributions. |
static CountsTable[] |
AnalyzeDisns.computeModelEntropies(Model model,
CountsTable[] entropy)
A method to compute a model's entropy statistics for all estimated distributions. |
static Set |
AnalyzeDisns.getFutures(Set futures,
Model model,
int level)
Returns all possible futures for the specified model at the specified back-off level, using the specified set for storage (the specified set is first cleared before futures are stored). |
static double[] |
AnalyzeDisns.getLogProbDisn(Model model,
int level,
Event hist,
Set futures,
double[] disn,
Transition tmpTrans)
Returns the smoothed log-probability distribution for the specified history at the specified back-off level in the specified model. |
static CountsTable[] |
AnalyzeDisns.newEntropyCountsTables(Model model)
Returns an array of CountsTable instances in which to store the
entropy of every history at every back-off level. |
static BiCountsTable[] |
AnalyzeDisns.newJSCountsTables(Model model)
Returns an array of BiCountsTable instances in which to store the
JS divergence of every history at every back-off level, both to the
previous back-off level and to the zeroeth back-off level. |
static void |
AnalyzeDisns.outputHistories(Model model)
A debugging method that outputs all histories of the specified model to System.out . |
static void |
PrintDisn.printLogProbDisn(PrintWriter writer,
ModelCollection mc,
Model model,
int level,
Event hist,
Set futures,
Transition tmpTrans)
Prints the log-probability distribution of the specified event at the specified back-off level of the specified model to the specified writer. |
void |
ModelCollection.set(Model lexPriorModel,
Model nonterminalPriorModel,
Model topNonterminalModel,
Model topLexModel,
Model headModel,
Model gapModel,
Model leftSubcatModel,
Model rightSubcatModel,
Model modNonterminalModel,
Model modWordModel,
CountsTable vocabCounter,
CountsTable wordFeatureCounter,
CountsTable nonterminals,
Map posMap,
Map headToParentMap,
Map leftSubcatMap,
Map rightSubcatMap,
Map modNonterminalMap,
Map simpleModNonterminalMap,
Set prunedPreterms,
Set prunedPunctuation,
FlexibleMap canonicalEvents)
Sets all the data members of this object. |
void |
Model.share(int backOffLevel,
Model otherModel,
int otherModelBackOffLevel)
Indicates to use counts or precomputed probabilities from the specified back-off level of this model when estimating probabilities for the specified back-off level of another model. |
static void |
AnalyzeDisns.writeKLDistStats(Model model)
Creates two files named after the probability structure of the specified model, and writes Kullback-Leibler divergences (relative entropies) between the zeroeth-level back-off distributions and the other back-off distributions to one file and writes Jensen-Shannon divergences between zeroeth-level back-off distributions and the other back-off distributions to the other file. |
static void |
AnalyzeDisns.writeModelStats(Model model)
Creates a file named after the probability structure class of the specified model and writes information about every distribution contained in that model. |
Uses of Model in danbikel.parser.ms |
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Methods in danbikel.parser.ms that return Model | |
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Model |
ModNonterminalModelStructure5.newModel()
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Model |
ModNonterminalModelStructure6.newModel()
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Model |
ModNonterminalModelStructure7.newModel()
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