nltk.tag.brill_trainer module

class nltk.tag.brill_trainer.BrillTaggerTrainer[source]

Bases: object

A trainer for tbl taggers.

__init__(initial_tagger, templates, trace=0, deterministic=None, ruleformat='str')[source]

Construct a Brill tagger from a baseline tagger and a set of templates

Parameters
  • initial_tagger (Tagger) – the baseline tagger

  • templates (list of Templates) – templates to be used in training

  • trace (int) – verbosity level

  • deterministic (bool) – if True, adjudicate ties deterministically

  • ruleformat (str) – format of reported Rules

Returns

An untrained BrillTagger

Return type

BrillTagger

train(train_sents, max_rules=200, min_score=2, min_acc=None)[source]

Trains the Brill tagger on the corpus train_sents, producing at most max_rules transformations, each of which reduces the net number of errors in the corpus by at least min_score, and each of which has accuracy not lower than min_acc.

>>> # Relevant imports
>>> from nltk.tbl.template import Template
>>> from nltk.tag.brill import Pos, Word
>>> from nltk.tag import untag, RegexpTagger, BrillTaggerTrainer
>>> # Load some data
>>> from nltk.corpus import treebank
>>> training_data = treebank.tagged_sents()[:100]
>>> baseline_data = treebank.tagged_sents()[100:200]
>>> gold_data = treebank.tagged_sents()[200:300]
>>> testing_data = [untag(s) for s in gold_data]
>>> backoff = RegexpTagger([
... (r'^-?[0-9]+(.[0-9]+)?$', 'CD'),   # cardinal numbers
... (r'(The|the|A|a|An|an)$', 'AT'),   # articles
... (r'.*able$', 'JJ'),                # adjectives
... (r'.*ness$', 'NN'),                # nouns formed from adjectives
... (r'.*ly$', 'RB'),                  # adverbs
... (r'.*s$', 'NNS'),                  # plural nouns
... (r'.*ing$', 'VBG'),                # gerunds
... (r'.*ed$', 'VBD'),                 # past tense verbs
... (r'.*', 'NN')                      # nouns (default)
... ])
>>> baseline = backoff #see NOTE1
>>> baseline.evaluate(gold_data) 
0.2450142...
>>> # Set up templates
>>> Template._cleartemplates() #clear any templates created in earlier tests
>>> templates = [Template(Pos([-1])), Template(Pos([-1]), Word([0]))]
>>> # Construct a BrillTaggerTrainer
>>> tt = BrillTaggerTrainer(baseline, templates, trace=3)
>>> tagger1 = tt.train(training_data, max_rules=10)
TBL train (fast) (seqs: 100; tokens: 2417; tpls: 2; min score: 2; min acc: None)
Finding initial useful rules...
    Found 845 useful rules.

           B      |
   S   F   r   O  |        Score = Fixed - Broken
   c   i   o   t  |  R     Fixed = num tags changed incorrect -> correct
   o   x   k   h  |  u     Broken = num tags changed correct -> incorrect
   r   e   e   e  |  l     Other = num tags changed incorrect -> incorrect
   e   d   n   r  |  e
------------------+-------------------------------------------------------
 132 132   0   0  | AT->DT if Pos:NN@[-1]
  85  85   0   0  | NN->, if Pos:NN@[-1] & Word:,@[0]
  69  69   0   0  | NN->. if Pos:NN@[-1] & Word:.@[0]
  51  51   0   0  | NN->IN if Pos:NN@[-1] & Word:of@[0]
  47  63  16 161  | NN->IN if Pos:NNS@[-1]
  33  33   0   0  | NN->TO if Pos:NN@[-1] & Word:to@[0]
  26  26   0   0  | IN->. if Pos:NNS@[-1] & Word:.@[0]
  24  24   0   0  | IN->, if Pos:NNS@[-1] & Word:,@[0]
  22  27   5  24  | NN->-NONE- if Pos:VBD@[-1]
  17  17   0   0  | NN->CC if Pos:NN@[-1] & Word:and@[0]
>>> tagger1.rules()[1:3]
(Rule('001', 'NN', ',', [(Pos([-1]),'NN'), (Word([0]),',')]), Rule('001', 'NN', '.', [(Pos([-1]),'NN'), (Word([0]),'.')]))
>>> train_stats = tagger1.train_stats()
>>> [train_stats[stat] for stat in ['initialerrors', 'finalerrors', 'rulescores']]
[1775, 1269, [132, 85, 69, 51, 47, 33, 26, 24, 22, 17]]
>>> tagger1.print_template_statistics(printunused=False)
TEMPLATE STATISTICS (TRAIN)  2 templates, 10 rules)
TRAIN (   2417 tokens) initial  1775 0.2656 final:  1269 0.4750
#ID | Score (train) |  #Rules     | Template
--------------------------------------------
001 |   305   0.603 |   7   0.700 | Template(Pos([-1]),Word([0]))
000 |   201   0.397 |   3   0.300 | Template(Pos([-1]))

>>> tagger1.evaluate(gold_data) 
0.43996...
>>> tagged, test_stats = tagger1.batch_tag_incremental(testing_data, gold_data)
>>> tagged[33][12:] == [('foreign', 'IN'), ('debt', 'NN'), ('of', 'IN'), ('$', 'NN'), ('64', 'CD'),
... ('billion', 'NN'), ('*U*', 'NN'), ('--', 'NN'), ('the', 'DT'), ('third-highest', 'NN'), ('in', 'NN'),
... ('the', 'DT'), ('developing', 'VBG'), ('world', 'NN'), ('.', '.')]
True
>>> [test_stats[stat] for stat in ['initialerrors', 'finalerrors', 'rulescores']]
[1855, 1376, [100, 85, 67, 58, 27, 36, 27, 16, 31, 32]]
>>> # A high-accuracy tagger
>>> tagger2 = tt.train(training_data, max_rules=10, min_acc=0.99)
TBL train (fast) (seqs: 100; tokens: 2417; tpls: 2; min score: 2; min acc: 0.99)
Finding initial useful rules...
    Found 845 useful rules.

           B      |
   S   F   r   O  |        Score = Fixed - Broken
   c   i   o   t  |  R     Fixed = num tags changed incorrect -> correct
   o   x   k   h  |  u     Broken = num tags changed correct -> incorrect
   r   e   e   e  |  l     Other = num tags changed incorrect -> incorrect
   e   d   n   r  |  e
------------------+-------------------------------------------------------
 132 132   0   0  | AT->DT if Pos:NN@[-1]
  85  85   0   0  | NN->, if Pos:NN@[-1] & Word:,@[0]
  69  69   0   0  | NN->. if Pos:NN@[-1] & Word:.@[0]
  51  51   0   0  | NN->IN if Pos:NN@[-1] & Word:of@[0]
  36  36   0   0  | NN->TO if Pos:NN@[-1] & Word:to@[0]
  26  26   0   0  | NN->. if Pos:NNS@[-1] & Word:.@[0]
  24  24   0   0  | NN->, if Pos:NNS@[-1] & Word:,@[0]
  19  19   0   6  | NN->VB if Pos:TO@[-1]
  18  18   0   0  | CD->-NONE- if Pos:NN@[-1] & Word:0@[0]
  18  18   0   0  | NN->CC if Pos:NN@[-1] & Word:and@[0]
>>> tagger2.evaluate(gold_data)  
0.44159544...
>>> tagger2.rules()[2:4]
(Rule('001', 'NN', '.', [(Pos([-1]),'NN'), (Word([0]),'.')]), Rule('001', 'NN', 'IN', [(Pos([-1]),'NN'), (Word([0]),'of')]))

# NOTE1: (!!FIXME) A far better baseline uses nltk.tag.UnigramTagger, # with a RegexpTagger only as backoff. For instance, # >>> baseline = UnigramTagger(baseline_data, backoff=backoff) # However, as of Nov 2013, nltk.tag.UnigramTagger does not yield consistent results # between python versions. The simplistic backoff above is a workaround to make doctests # get consistent input.

Parameters
  • train_sents (list(list(tuple))) – training data

  • max_rules (int) – output at most max_rules rules

  • min_score (int) – stop training when no rules better than min_score can be found

  • min_acc (float or None) – discard any rule with lower accuracy than min_acc

Returns

the learned tagger

Return type

BrillTagger