C# Class MSTParser.MSTParser

ファイルを表示 Open project: rasoolims/MSTParserCSharp

Public Methods

Method	Description
Evaluate ( string goldFile, string outFile ) : EvaluationResult	Evaluates the specified gold file (The file with Correct Dependecy Labels) with out file (the output file of the parser)
Parse ( string words, string posTags, DependencyParser parser, string &labels, int &deps ) : void	Parses the specified words with the specified trained parser.
Parse ( string words, string posTags, string modelName, bool labeled, int order, string &labels, int &deps ) : void	Parses the specified sentence composed of words.
Parse ( string words, string posTags, string modelName, bool labeled, string &labels, int &deps ) : void	Parses the specified words, order is by default 2
Parse ( string words, string posTags, string modelName, int order, string &labels, int &deps ) : void	Parses the specified words. Create forest is by default true
Parse ( string words, string posTags, string modelName, string &labels, int &deps ) : void	Parses the specified words, order is by default 2 and labeled is true
Test ( string testFile, string modelName, string outFile ) : void	Tests the specified test file, order is by default= 1
Test ( string testFile, string modelName, string outFile, int order ) : void	Tests the specified test file.
Train ( string trainFile, string modelName ) : void	Train the paser with train file, order is by default=1, createforest=true,trainingK=1,isProjective=true,numOfTrainingIterations=10
Train ( string trainFile, string modelName, int numOfTrainingIterations ) : void	Train the paser with train file, order is by default=1, createforest=true,trainingK=1,isProjective=true
Train ( string trainFile, string modelName, int numOfTrainingIterations, bool isProjective ) : void	Train the paser with train file, order is by default=1, createforest=true,trainingK=1
Train ( string trainFile, string modelName, int numOfTrainingIterations, bool isProjective, int trainingK ) : void	Train the paser with train file, order is by default=1, createforest=true
Train ( string trainFile, string modelName, int numOfTrainingIterations, bool isProjective, int trainingK, bool createForest ) : void	Train the paser with train file, order is by default=1
Train ( string trainFile, string modelName, int numOfTrainingIterations, bool isProjective, int trainingK, bool createForest, int order ) : void	Train the paser with train file.

Method Details

Evaluate() public static method

Evaluates the specified gold file (The file with Correct Dependecy Labels) with out file (the output file of the parser)

public static Evaluate ( string goldFile, string outFile ) : EvaluationResult
goldFile	string	The gold file path
outFile	string	The out file path
return	EvaluationResult

Parse() public static method

Parses the specified words with the specified trained parser.

public static Parse ( string words, string posTags, DependencyParser parser, string &labels, int &deps ) : void
words	string	The words.
posTags	string	The pos tags.
parser	DependencyParser	The trained parser.
labels	string	The labels.
deps	int	The deps.
return	void

Parse() public static method

Parses the specified sentence composed of words.

public static Parse ( string words, string posTags, string modelName, bool labeled, int order, string &labels, int &deps ) : void
words	string	The words.
posTags	string	The pos tags.
modelName	string	Name of the model.
labeled	bool	if set to true [labeled].
order	int	The order.
labels	string	The labels as an output.
deps	int	The deps as an output.
return	void

Parse() public static method

Parses the specified words, order is by default 2

public static Parse ( string words, string posTags, string modelName, bool labeled, string &labels, int &deps ) : void
words	string	The words.
posTags	string	The pos tags.
modelName	string	Name of the model.
labeled	bool	if set to true [labeled].
labels	string	The labels.
deps	int	The deps.
return	void

Parse() public static method

Parses the specified words. Create forest is by default true

public static Parse ( string words, string posTags, string modelName, int order, string &labels, int &deps ) : void
words	string	The words.
posTags	string	The pos tags.
modelName	string	Name of the model.
order	int	The order.
labels	string	The labels.
deps	int	The deps.
return	void

Parse() public static method

Parses the specified words, order is by default 2 and labeled is true

public static Parse ( string words, string posTags, string modelName, string &labels, int &deps ) : void
words	string	The words.
posTags	string	The pos tags.
modelName	string	Name of the model.
labels	string	The labels.
deps	int	The deps.
return	void

Test() public static method

Tests the specified test file, order is by default= 1

public static Test ( string testFile, string modelName, string outFile ) : void
testFile	string	The test file.
modelName	string	Model Path
outFile	string	The parser output path
return	void

Test() public static method

Tests the specified test file.

public static Test ( string testFile, string modelName, string outFile, int order ) : void
testFile	string	The test file.
modelName	string	Model Path
outFile	string	The parser output path
order	int	is either 1 or 2 /// Default is 1 /// Specifies the order/scope of features. 1 only has features over single edges /// and 2 has features over pairs of adjacent edges in the tree
return	void

Train() public static method

Train the paser with train file, order is by default=1, createforest=true,trainingK=1,isProjective=true,numOfTrainingIterations=10

public static Train ( string trainFile, string modelName ) : void
trainFile	string	Training file path
modelName	string	Model Path
return	void

Train() public static method

Train the paser with train file, order is by default=1, createforest=true,trainingK=1,isProjective=true

public static Train ( string trainFile, string modelName, int numOfTrainingIterations ) : void
trainFile	string	Training file path
modelName	string	Model Path
numOfTrainingIterations	int	Number of iteration for training feature weights; Default=10
return	void

Train() public static method

Train the paser with train file, order is by default=1, createforest=true,trainingK=1

public static Train ( string trainFile, string modelName, int numOfTrainingIterations, bool isProjective ) : void
trainFile	string	Training file path
modelName	string	Model Path
numOfTrainingIterations	int	Number of iteration for training feature weights; Default=10
isProjective	bool	Type of Parsing; Default is True; Free order languages need to be nonprojective /// True: use the projective parsing algorithm during training i.e. The Eisner algorithm /// False: use the non-projective parsing algorithm during training i.e. The Chu-Liu-Edmonds algorithm
return	void

Train() public static method

Train the paser with train file, order is by default=1, createforest=true

public static Train ( string trainFile, string modelName, int numOfTrainingIterations, bool isProjective, int trainingK ) : void
trainFile	string	Training file path
modelName	string	Model Path
numOfTrainingIterations	int	Number of iteration for training feature weights; Default=10
isProjective	bool	Type of Parsing; Default is True; Free order languages need to be nonprojective /// True: use the projective parsing algorithm during training i.e. The Eisner algorithm /// False: use the non-projective parsing algorithm during training i.e. The Chu-Liu-Edmonds algorithm
trainingK	int	Specifies the k-best parse set size to create constraints during training /// Default is 1 /// For non-projective parsing algorithm, k-best decoding is approximate
return	void

Train() public static method

Train the paser with train file, order is by default=1

public static Train ( string trainFile, string modelName, int numOfTrainingIterations, bool isProjective, int trainingK, bool createForest ) : void
trainFile	string	Training file path
modelName	string	Model Path
numOfTrainingIterations	int	Number of iteration for training feature weights; Default=10
isProjective	bool	Type of Parsing; Default is True; Free order languages need to be nonprojective /// True: use the projective parsing algorithm during training i.e. The Eisner algorithm /// False: use the non-projective parsing algorithm during training i.e. The Chu-Liu-Edmonds algorithm
trainingK	int	Specifies the k-best parse set size to create constraints during training /// Default is 1 /// For non-projective parsing algorithm, k-best decoding is approximate
createForest	bool	Default is "true" /// If create-forest is false, it will not create the training parse forest . It assumes it has been created. /// This flag is useful if you are training many models on the same data and /// features but using different Parameters (e.g. training iters, decoding type).
return	void

Train() public static method

Train the paser with train file.

public static Train ( string trainFile, string modelName, int numOfTrainingIterations, bool isProjective, int trainingK, bool createForest, int order ) : void
trainFile	string	Training file path
modelName	string	Model Path
numOfTrainingIterations	int	Number of iteration for training feature weights; Default=10
isProjective	bool	Type of Parsing; Default is True; Free order languages need to be nonprojective /// True: use the projective parsing algorithm during training i.e. The Eisner algorithm /// False: use the non-projective parsing algorithm during training i.e. The Chu-Liu-Edmonds algorithm
trainingK	int	Specifies the k-best parse set size to create constraints during training /// Default is 1 /// For non-projective parsing algorithm, k-best decoding is approximate
createForest	bool	Default is "true" /// If create-forest is false, it will not create the training parse forest . It assumes it has been created. /// This flag is useful if you are training many models on the same data and /// features but using different Parameters (e.g. training iters, decoding type).
order	int	is either 1 or 2 /// Default is 1 /// Specifies the order/scope of features. 1 only has features over single edges /// and 2 has features over pairs of adjacent edges in the tree
return	void