NLP | Brill Tagger
- brillitagger 类是一个基于转化的 tagger 。它不是 SequentialBackoffTagger 的子类。
- 此外，它使用一系列规则来纠正初始标记器的结果。
- 它遵循的这些规则是基于分数的。这个分数等于他们改正的错误数减去他们产生的新错误数。
代码#1:训练一个高明的班级
# Loading Libraries
from nltk.tag import brill, brill_trainer
def train_brill_tagger(initial_tagger, train_sents, **kwargs):
templates = [
brill.Template(brill.Pos([-1])),
brill.Template(brill.Pos([1])),
brill.Template(brill.Pos([-2])),
brill.Template(brill.Pos([2])),
brill.Template(brill.Pos([-2, -1])),
brill.Template(brill.Pos([1, 2])),
brill.Template(brill.Pos([-3, -2, -1])),
brill.Template(brill.Pos([1, 2, 3])),
brill.Template(brill.Pos([-1]), brill.Pos([1])),
brill.Template(brill.Word([-1])),
brill.Template(brill.Word([1])),
brill.Template(brill.Word([-2])),
brill.Template(brill.Word([2])),
brill.Template(brill.Word([-2, -1])),
brill.Template(brill.Word([1, 2])),
brill.Template(brill.Word([-3, -2, -1])),
brill.Template(brill.Word([1, 2, 3])),
brill.Template(brill.Word([-1]), brill.Word([1])),
]
# Using BrillTaggerTrainer to train
trainer = brill_trainer.BrillTaggerTrainer(
initial_tagger, templates, deterministic = True)
return trainer.train(train_sents, **kwargs)
代码#2:让我们使用训练有素的布里尔塔格
from nltk.tag import brill, brill_trainer
from nltk.tag import DefaultTagger
from nltk.corpus import treebank
from tag_util import train_brill_tagger
# Initializing
default_tag = DefaultTagger('NN')
# initializing training and testing set
train_data = treebank.tagged_sents()[:3000]
test_data = treebank.tagged_sents()[3000:]
initial_tag = backoff_tagger(
train_data, [UnigramTagger, BigramTagger,
TrigramTagger], backoff = default_tagger)
a = initial_tag.evaluate(test_data)
print ("Accuracy of Initial Tag : ", a)
输出:
Accuracy of Initial Tag : 0.8806820634578028
代码#3 :
brill_tag = train_brill_tagger(initial_tag, train_data)
b = brill_tag.evaluate(test_data)
print ("Accuracy of brill_tag : ", b)
输出:
Accuracy of brill_tag : 0.8827541549751781
