nltk.tag.api module

Interface for tagging each token in a sentence with supplementary information, such as its part of speech.

class nltk.tag.api.TaggerI[source]

Bases: object

A processing interface for assigning a tag to each token in a list. Tags are case sensitive strings that identify some property of each token, such as its part of speech or its sense.

Some taggers require specific types for their tokens. This is generally indicated by the use of a sub-interface to TaggerI. For example, featureset taggers, which are subclassed from FeaturesetTagger, require that each token be a featureset.

Subclasses must define:
  • either tag() or tag_sents() (or both)

abstract tag(tokens)[source]

Determine the most appropriate tag sequence for the given token sequence, and return a corresponding list of tagged tokens. A tagged token is encoded as a tuple (token, tag).

Return type

list(tuple(str, str))

tag_sents(sentences)[source]

Apply self.tag() to each element of sentences. I.e.:

return [self.tag(sent) for sent in sentences]

evaluate(gold)[source]

Score the accuracy of the tagger against the gold standard. Strip the tags from the gold standard text, retag it using the tagger, then compute the accuracy score.

Parameters

gold (list(list(tuple(str, str)))) – The list of tagged sentences to score the tagger on.

Return type

float

class nltk.tag.api.FeaturesetTaggerI[source]

Bases: nltk.tag.api.TaggerI

A tagger that requires tokens to be featuresets. A featureset is a dictionary that maps from feature names to feature values. See nltk.classify for more information about features and featuresets.