nltk.tag.ClassifierBasedTagger¶

class nltk.tag.ClassifierBasedTagger[source]¶

Bases: SequentialBackoffTagger, FeaturesetTaggerI

A sequential tagger that uses a classifier to choose the tag for each token in a sentence. The featureset input for the classifier is generated by a feature detector function:

feature_detector(tokens, index, history) -> featureset

Where tokens is the list of unlabeled tokens in the sentence; index is the index of the token for which feature detection should be performed; and history is list of the tags for all tokens before index.

Construct a new classifier-based sequential tagger.

Parameters

feature_detector – A function used to generate the featureset input for the classifier:: feature_detector(tokens, index, history) -> featureset
train – A tagged corpus consisting of a list of tagged sentences, where each sentence is a list of (word, tag) tuples.
backoff – A backoff tagger, to be used by the new tagger if it encounters an unknown context.
classifier_builder – A function used to train a new classifier based on the data in train. It should take one argument, a list of labeled featuresets (i.e., (featureset, label) tuples).
classifier – The classifier that should be used by the tagger. This is only useful if you want to manually construct the classifier; normally, you would use train instead.
backoff – A backoff tagger, used if this tagger is unable to determine a tag for a given token.
cutoff_prob – If specified, then this tagger will fall back on its backoff tagger if the probability of the most likely tag is less than cutoff_prob.

__init__(feature_detector=None, train=None, classifier_builder=<bound method NaiveBayesClassifier.train of <class 'nltk.classify.naivebayes.NaiveBayesClassifier'>>, classifier=None, backoff=None, cutoff_prob=None, verbose=False)[source]¶

choose_tag(tokens, index, history)[source]¶

Decide which tag should be used for the specified token, and return that tag. If this tagger is unable to determine a tag for the specified token, return None – do not consult the backoff tagger. This method should be overridden by subclasses of SequentialBackoffTagger.

Return type

str

Parameters

tokens (list) – The list of words that are being tagged.
index (int) – The index of the word whose tag should be returned.
history (list(str)) – A list of the tags for all words before index.

feature_detector(tokens, index, history)[source]¶

Return the feature detector that this tagger uses to generate featuresets for its classifier. The feature detector is a function with the signature:

feature_detector(tokens, index, history) -> featureset

See classifier()

accuracy(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

property backoff¶: The backoff tagger for this tagger.

classifier()[source]¶: Return the classifier that this tagger uses to choose a tag for each word in a sentence. The input for this classifier is generated using this tagger’s feature detector. See feature_detector()

confusion(gold)[source]¶

Return a ConfusionMatrix with the tags from gold as the reference values, with the predictions from tag_sents as the predicted values.

>>> from nltk.tag import PerceptronTagger
>>> from nltk.corpus import treebank
>>> tagger = PerceptronTagger()
>>> gold_data = treebank.tagged_sents()[:10]
>>> print(tagger.confusion(gold_data))
       |        -                                                                                     |
       |        N                                                                                     |
       |        O                                               P                                     |
       |        N                       J  J        N  N  P  P  R     R           V  V  V  V  V  W    |
       |  '     E     C  C  D  E  I  J  J  J  M  N  N  N  O  R  P  R  B  R  T  V  B  B  B  B  B  D  ` |
       |  '  ,  -  .  C  D  T  X  N  J  R  S  D  N  P  S  S  P  $  B  R  P  O  B  D  G  N  P  Z  T  ` |
-------+----------------------------------------------------------------------------------------------+
    '' | <1> .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . |
     , |  .<15> .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . |
-NONE- |  .  . <.> .  .  2  .  .  .  2  .  .  .  5  1  .  .  .  .  2  .  .  .  .  .  .  .  .  .  .  . |
     . |  .  .  .<10> .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . |
    CC |  .  .  .  . <1> .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . |
    CD |  .  .  .  .  . <5> .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . |
    DT |  .  .  .  .  .  .<20> .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . |
    EX |  .  .  .  .  .  .  . <1> .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . |
    IN |  .  .  .  .  .  .  .  .<22> .  .  .  .  .  .  .  .  .  .  3  .  .  .  .  .  .  .  .  .  .  . |
    JJ |  .  .  .  .  .  .  .  .  .<16> .  .  .  .  1  .  .  .  .  1  .  .  .  .  .  .  .  .  .  .  . |
   JJR |  .  .  .  .  .  .  .  .  .  . <.> .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . |
   JJS |  .  .  .  .  .  .  .  .  .  .  . <1> .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . |
    MD |  .  .  .  .  .  .  .  .  .  .  .  . <1> .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . |
    NN |  .  .  .  .  .  .  .  .  .  .  .  .  .<28> 1  1  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . |
   NNP |  .  .  .  .  .  .  .  .  .  .  .  .  .  .<25> .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . |
   NNS |  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .<19> .  .  .  .  .  .  .  .  .  .  .  .  .  .  . |
   POS |  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . <1> .  .  .  .  .  .  .  .  .  .  .  .  .  . |
   PRP |  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . <4> .  .  .  .  .  .  .  .  .  .  .  .  . |
  PRP$ |  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . <2> .  .  .  .  .  .  .  .  .  .  .  . |
    RB |  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . <4> .  .  .  .  .  .  .  .  .  .  . |
   RBR |  .  .  .  .  .  .  .  .  .  .  1  .  .  .  .  .  .  .  .  . <1> .  .  .  .  .  .  .  .  .  . |
    RP |  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . <1> .  .  .  .  .  .  .  .  . |
    TO |  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . <5> .  .  .  .  .  .  .  . |
    VB |  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . <3> .  .  .  .  .  .  . |
   VBD |  .  .  .  .  .  .  .  .  .  .  .  .  .  1  .  .  .  .  .  .  .  .  .  . <6> .  .  .  .  .  . |
   VBG |  .  .  .  .  .  .  .  .  .  .  .  .  .  1  .  .  .  .  .  .  .  .  .  .  . <4> .  .  .  .  . |
   VBN |  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  1  . <4> .  .  .  . |
   VBP |  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . <3> .  .  . |
   VBZ |  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . <7> .  . |
   WDT |  .  .  .  .  .  .  .  .  2  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . <.> . |
    `` |  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  . <1>|
-------+----------------------------------------------------------------------------------------------+
(row = reference; col = test)

Parameters: gold (list(list(tuple(str, str)))) – The list of tagged sentences to run the tagger with, also used as the reference values in the generated confusion matrix.
Return type: ConfusionMatrix

evaluate(**kwargs)[source]¶: @deprecated: Use accuracy(gold) instead.

evaluate_per_tag(gold, alpha=0.5, truncate=None, sort_by_count=False)[source]¶

Tabulate the recall, precision and f-measure for each tag from gold or from running tag on the tokenized sentences from gold.

>>> from nltk.tag import PerceptronTagger
>>> from nltk.corpus import treebank
>>> tagger = PerceptronTagger()
>>> gold_data = treebank.tagged_sents()[:10]
>>> print(tagger.evaluate_per_tag(gold_data))
   Tag | Prec.  | Recall | F-measure
-------+--------+--------+-----------
    '' | 1.0000 | 1.0000 | 1.0000
     , | 1.0000 | 1.0000 | 1.0000
-NONE- | 0.0000 | 0.0000 | 0.0000
     . | 1.0000 | 1.0000 | 1.0000
    CC | 1.0000 | 1.0000 | 1.0000
    CD | 0.7143 | 1.0000 | 0.8333
    DT | 1.0000 | 1.0000 | 1.0000
    EX | 1.0000 | 1.0000 | 1.0000
    IN | 0.9167 | 0.8800 | 0.8980
    JJ | 0.8889 | 0.8889 | 0.8889
   JJR | 0.0000 | 0.0000 | 0.0000
   JJS | 1.0000 | 1.0000 | 1.0000
    MD | 1.0000 | 1.0000 | 1.0000
    NN | 0.8000 | 0.9333 | 0.8615
   NNP | 0.8929 | 1.0000 | 0.9434
   NNS | 0.9500 | 1.0000 | 0.9744
   POS | 1.0000 | 1.0000 | 1.0000
   PRP | 1.0000 | 1.0000 | 1.0000
  PRP$ | 1.0000 | 1.0000 | 1.0000
    RB | 0.4000 | 1.0000 | 0.5714
   RBR | 1.0000 | 0.5000 | 0.6667
    RP | 1.0000 | 1.0000 | 1.0000
    TO | 1.0000 | 1.0000 | 1.0000
    VB | 1.0000 | 1.0000 | 1.0000
   VBD | 0.8571 | 0.8571 | 0.8571
   VBG | 1.0000 | 0.8000 | 0.8889
   VBN | 1.0000 | 0.8000 | 0.8889
   VBP | 1.0000 | 1.0000 | 1.0000
   VBZ | 1.0000 | 1.0000 | 1.0000
   WDT | 0.0000 | 0.0000 | 0.0000
    `` | 1.0000 | 1.0000 | 1.0000

Parameters

gold (list(list(tuple(str, str)))) – The list of tagged sentences to score the tagger on.
alpha (float) – Ratio of the cost of false negative compared to false positives, as used in the f-measure computation. Defaults to 0.5, where the costs are equal.
truncate (int, optional) – If specified, then only show the specified number of values. Any sorting (e.g., sort_by_count) will be performed before truncation. Defaults to None
sort_by_count (bool, optional) – Whether to sort the outputs on number of occurrences of that tag in the gold data, defaults to False

Returns

A tabulated recall, precision and f-measure string

Return type

str

f_measure(gold, alpha=0.5)[source]¶

Compute the f-measure for each tag from gold or from running tag on the tokenized sentences from gold. Then, return the dictionary with mappings from tag to f-measure. The f-measure is the harmonic mean of the precision and recall, weighted by alpha. In particular, given the precision p and recall r defined by:

p = true positive / (true positive + false negative)
r = true positive / (true positive + false positive)

The f-measure is:

1/(alpha/p + (1-alpha)/r)

With alpha = 0.5, this reduces to:

2pr / (p + r)

Parameters

gold (list(list(tuple(str, str)))) – The list of tagged sentences to score the tagger on.
alpha (float) – Ratio of the cost of false negative compared to false positives. Defaults to 0.5, where the costs are equal.

Returns

A mapping from tags to precision

Return type

Dict[str, float]

precision(gold)[source]¶

Compute the precision for each tag from gold or from running tag on the tokenized sentences from gold. Then, return the dictionary with mappings from tag to precision. The precision is defined as:

p = true positive / (true positive + false negative)

Parameters: gold (list(list(tuple(str, str)))) – The list of tagged sentences to score the tagger on.
Returns: A mapping from tags to precision
Return type: Dict[str, float]

recall(gold) → Dict[str, float][source]¶

Compute the recall for each tag from gold or from running tag on the tokenized sentences from gold. Then, return the dictionary with mappings from tag to recall. The recall is defined as:

r = true positive / (true positive + false positive)

Parameters: gold (list(list(tuple(str, str)))) – The list of tagged sentences to score the tagger on.
Returns: A mapping from tags to recall
Return type: Dict[str, float]

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_one(tokens, index, history)[source]¶

Determine an appropriate tag for the specified token, and return that tag. If this tagger is unable to determine a tag for the specified token, then its backoff tagger is consulted.

Return type

str

Parameters

tokens (list) – The list of words that are being tagged.
index (int) – The index of the word whose tag should be returned.
history (list(str)) – A list of the tags for all words before index.

tag_sents(sentences)[source]¶

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

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

NLTK

Documentation

nltk.tag.ClassifierBasedTagger¶