Parsing Engine

Uses of Class
danbikel.parser.Model

Packages that use Model
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
 

Subclasses of Model in danbikel.parser
 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
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
           
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
 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
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
 

Methods in danbikel.parser.ms that return Model
 Model ModNonterminalModelStructure5.newModel()
           
 Model ModNonterminalModelStructure6.newModel()
           
 Model ModNonterminalModelStructure7.newModel()
           
 


Parsing Engine

Author: Dan Bikel.