Source code for nltk.tag.api

# Natural Language Toolkit: Tagger Interface
# Copyright (C) 2001-2015 NLTK Project
# Author: Edward Loper <>
#         Steven Bird <> (minor additions)
# URL: <>
# For license information, see LICENSE.TXT

Interface for tagging each token in a sentence with supplementary
information, such as its part of speech.
from nltk.internals import overridden
from nltk.metrics import accuracy

from nltk.tag.util import untag

[docs]class TaggerI(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) """
[docs] def tag(self, tokens): """ 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)``. :rtype: list(tuple(str, str)) """ if overridden(self.tag_sents): return self.tag_sents([tokens])[0] else: raise NotImplementedError()
[docs] def tag_sents(self, sentences): """ Apply ``self.tag()`` to each element of *sentences*. I.e.: return [self.tag(sent) for sent in sentences] """ return [self.tag(sent) for sent in sentences]
[docs] def evaluate(self, gold): """ 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. :type gold: list(list(tuple(str, str))) :param gold: The list of tagged sentences to score the tagger on. :rtype: float """ tagged_sents = self.tag_sents(untag(sent) for sent in gold) gold_tokens = sum(gold, []) test_tokens = sum(tagged_sents, []) return accuracy(gold_tokens, test_tokens)
def _check_params(self, train, model): if (train and model) or (not train and not model): raise ValueError('Must specify either training data or trained model.')
class FeaturesetTaggerI(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. """