org.allenai.nlpstack.parse.poly.polyparser
the data source for the parse trees
identifies the ClassificationTask associated with each feature vector
the transition system to use (for generating states)
a trained cost function to adapt (optional)
a trained cost function to adapt (optional)
This generates a list of labeled feature vectors from a gold parse tree (for training).
This generates a list of labeled feature vectors from a gold parse tree (for training). The gold parse tree is reduced to its representation as a list of 2*n transitions, then a TrainingVector is produced for each transition (in order).
Note that this function is implemented using tail-recursion.
the marble block
a list of training vectors
the data source for the parse trees
identifies the ClassificationTask associated with each feature vector
the transition system to use (for generating states)
A GoldParseTrainingVectorSource reduces a gold parse tree to a set of feature vectors for classifier training.
Essentially, we derive the 2*n parser states that lead to the gold parse. Each of these states becomes a feature vector (using the apply method of the provided TransitionParserFeature), labeled with the transition executed from that state in the gold parse.
One of the constructor arguments is a TaskIdentifer. This will dispatch the feature vectors to train different classifiers. For instance, if taskIdentifier(state) != taskIdentifier(state2), then their respective feature vectors (i.e. feature(state) and feature(state2)) will be used to train different classifiers.
the data source for the parse trees
identifies the ClassificationTask associated with each feature vector
the transition system to use (for generating states)
a trained cost function to adapt (optional)