nltk.tag.SennaNERTagger

class nltk.tag.SennaNERTagger[source]

Bases: Senna

__init__(path, encoding='utf-8')[source]
tag_sents(sentences)[source]

Applies the tag method over a list of sentences. This method will return for each sentence a list of tuples of (word, tag).

SUPPORTED_OPERATIONS = ['pos', 'chk', 'ner']
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

executable(base_path)[source]

The function that determines the system specific binary that should be used in the pipeline. In case, the system is not known the default senna binary will be used.

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]

Applies the specified operation(s) on a list of tokens.