Package nltk :: Package tag :: Module api :: Class TaggerI

type TaggerI

object --+
         |
        TaggerI

Known Subclasses:

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 FeaturesetTaggerI, require that each token be a featureset.

Subclasses must define:

either tag() or batch_tag() (or both)

Instance Methods

[hide private]

list of (token, tag)

tag(self, tokens)
Determine the most appropriate tag sequence for the given token sequence, and return a corresponding list of tagged tokens.

source code

batch_tag(self, sentences)
Apply self.tag() to each element of sentences. source code

float

evaluate(self, gold)
Score the accuracy of the tagger against the gold standard.

source code

_check_params(self, train, model)

source code

Method Details

[hide private]

tag(self, tokens)

source code

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).

Returns: list of (token, tag)

batch_tag(self, sentences)

source code

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

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

evaluate(self, gold)

source code

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 of list of (token, tag)) - The list of tagged sentences to score the tagger on.

Returns: float