nltk.tag.CRFTagger

class nltk.tag.CRFTagger[source]

Bases: TaggerI

A module for POS tagging using CRFSuite https://pypi.python.org/pypi/python-crfsuite

>>> from nltk.tag import CRFTagger
>>> ct = CRFTagger()

>>> train_data = [[('University','Noun'), ('is','Verb'), ('a','Det'), ('good','Adj'), ('place','Noun')],
... [('dog','Noun'),('eat','Verb'),('meat','Noun')]]

>>> ct.train(train_data,'model.crf.tagger')
>>> ct.tag_sents([['dog','is','good'], ['Cat','eat','meat']])
[[('dog', 'Noun'), ('is', 'Verb'), ('good', 'Adj')], [('Cat', 'Noun'), ('eat', 'Verb'), ('meat', 'Noun')]]

>>> gold_sentences = [[('dog','Noun'),('is','Verb'),('good','Adj')] , [('Cat','Noun'),('eat','Verb'), ('meat','Noun')]]
>>> ct.accuracy(gold_sentences)
1.0

Setting learned model file >>> ct = CRFTagger() >>> ct.set_model_file(‘model.crf.tagger’) >>> ct.accuracy(gold_sentences) 1.0

__init__(feature_func=None, verbose=False, training_opt={})[source]

Initialize the CRFSuite tagger

Parameters

  • feature_func – The function that extracts features for each token of a sentence. This function should take 2 parameters: tokens and index which extract features at index position from tokens list. See the build in _get_features function for more detail.

  • verbose (boolean) – output the debugging messages during training.

  • training_opt (dictionary) – python-crfsuite training options

Set of possible training options (using LBFGS training algorithm).
‘feature.minfreq’

The minimum frequency of features.

‘feature.possible_states’

Force to generate possible state features.

‘feature.possible_transitions’

Force to generate possible transition features.

‘c1’

Coefficient for L1 regularization.

‘c2’

Coefficient for L2 regularization.

‘max_iterations’

The maximum number of iterations for L-BFGS optimization.

‘num_memories’

The number of limited memories for approximating the inverse hessian matrix.

‘epsilon’

Epsilon for testing the convergence of the objective.

‘period’

The duration of iterations to test the stopping criterion.

‘delta’

The threshold for the stopping criterion; an L-BFGS iteration stops when the improvement of the log likelihood over the last ${period} iterations is no greater than this threshold.

‘linesearch’

The line search algorithm used in L-BFGS updates:

  • ‘MoreThuente’: More and Thuente’s method,

  • ‘Backtracking’: Backtracking method with regular Wolfe condition,

  • ‘StrongBacktracking’: Backtracking method with strong Wolfe condition

‘max_linesearch’

The maximum number of trials for the line search algorithm.

set_model_file(model_file)[source]
tag_sents(sents)[source]

Tag a list of sentences. NB before using this function, user should specify the mode_file either by

  • Train a new model using train function

  • Use the pre-trained model which is set via set_model_file function

Params sentences

list of sentences needed to tag.

Returns

list of tagged sentences.

Return type

list(list(tuple(str,str)))

train(train_data, model_file)[source]

Train the CRF tagger using CRFSuite :params train_data : is the list of annotated sentences. :type train_data : list (list(tuple(str,str))) :params model_file : the model will be saved to this file.

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

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]

Tag a sentence using Python CRFSuite Tagger. NB before using this function, user should specify the mode_file either by

  • Train a new model using train function

  • Use the pre-trained model which is set via set_model_file function

Params tokens

list of tokens needed to tag.

Returns

list of tagged tokens.

Return type

list(tuple(str,str))