nltk.tag.brill_trainer module¶
- class nltk.tag.brill_trainer.BrillTaggerTrainer[source]¶
Bases:
objectA 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:
- 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.accuracy(gold_data) 0.243...
>>> # 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 847 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 16 162 | 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']] [1776, 1270, [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 1776 0.2652 final: 1270 0.4746 #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]))
>>> round(tagger1.accuracy(gold_data),5) 0.43834
>>> 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']] [1859, 1380, [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 847 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]
>>> round(tagger2.accuracy(gold_data), 8) 0.43996744
>>> 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: