Package edu.stanford.nlp.ie

Class Summary

AbstractSequenceClassifier<IN extends CoreMap>	This class provides common functionality for (probabilistic) sequence models.
AcquisitionsPrior<IN extends CoreMap>
ClassifierCombiner<IN extends CoreMap & HasWord>	Merges the outputs of two or more AbstractSequenceClassifiers according to a simple precedence scheme: any given base classifier contributes only classifications of labels that do not exist in the base classifiers specified before, and that do not have any token overlap with labels assigned by higher priority classifiers.
EmpiricalNERPrior<IN extends CoreMap>
EntityCachingAbstractSequencePrior<IN extends CoreMap>	This class keeps track of all labeled entities and updates the its list whenever the label at a point gets changed.
NERClassifierCombiner	Subclass of ClassifierCombiner that behaves like a NER, by copying the AnswerAnnotation labels to NERAnnotation Also, it runs an additional classifier (QuantifiableEntityNormalizer) to recognize numeric entities
NERFeatureFactory<IN extends CoreLabel>	Features for Named Entity Recognition.
NumberNormalizer	Provides functions for converting words to numbers Unlike QuantifiableEntityNormalizer that normalizes various types of quantifiable entities like money and dates, NumberNormalizer only normalizes numeric expressions (e.g.
QuantifiableEntityNormalizer	Various methods for normalizing Money, Date, Percent, Time, and Number, Ordinal amounts.
SeminarsPrior<IN extends CoreMap>
UniformPrior<IN extends CoreMap>	Uniform prior to be used for generic Gibbs inference in the ie.crf.CRFClassifier