An ambiguity class for a word is the word by itself or its set of observed tags.
A collection of Ambiguity Class.
A simple data structure for some tag counts.
Maintains a map from words to tags and their counts.
This class is the same as a regular Extractor, but keeps a pointer to the tagger's dictionary as well.
Keeps track of a distributional similarity mapping, eg a map from word to class.
This class serves as the base class for classes which extract relevant information from a history to give it to the features.
Extractor for adding distsim information.
Extractor for adding a conjunction of distsim information.
This class contains the basic feature extractors used for all words and tag sequences (and interaction terms) for the MaxentTagger, but not the feature extractors explicitly targeting generalization for rare or unknown words.
This class contains feature extractors for the MaxentTagger that are only applied to rare (low frequency/unknown) words.
Maintains a set of feature extractors and applies them.
Look for verbs selecting a VBN verb.
Stores a triple of an extractor ID, a feature value (derived from history) and a y (tag) value.
Notes: This maintains a two way lookup between a History and an Integer index.
This module does the working out of lambda parameters for binary tagger features.
The main class for users to run, train, and test the part of speech tagger.
A simple class that maintains a list of WordTag pairs which are interned as they are added.
Reads tagged data from a file and creates a dictionary.
Reads and stores configuration information for a POS tagger.
This class represents the training samples.
Holds a Tagger Feature for the loglinear model.
This class contains POS tagger specific features.
Tags data and can handle either data with gold-standard tags (computing performance statistics) or unlabeled data.
This class holds the POS tags, assigns them unique ids, and knows which tags are open versus closed class.
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