用英语以外的其他语言为 Brill Tagger 训练语料库

Question

有没有人知道任何标记语料库或词典可以在英语以外的其他语言中使用 Brill 词性 (POS) 标记器？

谢谢！

Answer 1

如果您使用NLTK (http://nltk.org/) 并使用python 进行编码，您可以执行以下操作。您甚至不需要编写自己的 brill-tagger 代码，因为它已经在库中，http://nltk.org/_modules/nltk/tag/brill.html。

def train_brill_tagger(train_data):
    # Modules for creating the templates.
    from nltk.tag import UnigramTagger
    from nltk.tag.brill import SymmetricProximateTokensTemplate, ProximateTokensTemplate
    from nltk.tag.brill import ProximateTagsRule, ProximateWordsRule
    # The brill tagger module in NLTK.
    from nltk.tag.brill import FastBrillTaggerTrainer
    unigram_tagger = UnigramTagger(train_data)
    templates = [SymmetricProximateTokensTemplate(ProximateTagsRule, (1,1)),
                            SymmetricProximateTokensTemplate(ProximateTagsRule, (2,2)),
                            SymmetricProximateTokensTemplate(ProximateTagsRule, (1,2)),
                            SymmetricProximateTokensTemplate(ProximateTagsRule, (1,3)),
                            SymmetricProximateTokensTemplate(ProximateWordsRule, (1,1)),
                            SymmetricProximateTokensTemplate(ProximateWordsRule, (2,2)),
                            SymmetricProximateTokensTemplate(ProximateWordsRule, (1,2)),
                            SymmetricProximateTokensTemplate(ProximateWordsRule, (1,3)),
                            ProximateTokensTemplate(ProximateTagsRule, (-1, -1), (1,1)),
                            ProximateTokensTemplate(ProximateWordsRule, (-1, -1), (1,1))]

    trainer = FastBrillTaggerTrainer(initial_tagger=unigram_tagger,
                                   templates=templates, trace=3,
                                   deterministic=True)
    brill_tagger = trainer.train(train_data, max_rules=10)
    print
    return brill_tagger

# To train and test using Brown Corpus.
from nltk.corpus import brown
brown_train = list(brown.tagged_sents(categories='news')[:500])
brown_test = list(brown.tagged_sents(categories='news')[500:600])
brown501 = brown.tagged_sents(categories='news')[501]

bt = train_brill_tagger(brown_train)

# To tag one sentence.
print bt.tag(brown501)
print

# To evaluate tagger.
print 'Accuracy of Brill Tagger:', bt.evaluate(brown_test)

有一个语料库列表，语料库阅读器已经预编码在NLTK：http://nltk.googlecode.com/svn/trunk/nltk_data/index.xml

这是一个将 brill 标记器应用于荷兰语语料库的示例：

# To train and test using Alpino Corpus (Dutch).
from nltk.corpus import alpino
alpino_tagged_sents = alpino.tagged_sents()
# Split corpus into train/test.
datasize = len(alpino_tagged_sents)
trainsize = int(datasize*90/float(100))
alpino_train = list(alpino_tagged_sents[:trainsize])
alpino_test = list(alpino_tagged_sents[trainsize+1:])
alpinotest1 = [i for i,j in alpino_tagged_sents[trainsize+1]]

bt_nld = train_brill_tagger(alpino_train)
print 'Test sentence:', alpinotest
print bt_nld.tag(alpinotest1)
print
print bt_nld.evaluate(alpino_test)
print

事实上，如果你足够努力地阅读到这一点，这里有一个技巧，只需输入语料库就可以在 NLTK 中训练一个聪明的标注器 =)

from nltk.corpus import LazyCorpusLoader

def train_brill_with_corpus(nltkcorpus, train_percent = 90/float(100)):
    if not isinstance(nltkcorpus, LazyCorpusLoader):
        raise NameError("Please use a pre-coded corpus from NLTK.")

    tagged_sents = nltkcorpus.tagged_sents()
    if not tagged_sents: # i.e. tagged_sents == []
        raise NameError("This corpus doesn't have POS tags.")
    trainsize = int(train_percent*len(tagged_sents))
    corpus_train = list(tagged_sents[:trainsize])
    corpus_test = list(tagged_sents[trainsize+1:])
    bt = train_brill_tagger(corpus_train)
    return bt, corpus_test  

基本上使用train_brill_tagger() 和train_brill_with_corpus()，您可以这样做：

# To train and test using CESS_ESP Corpus (Spanish).
from nltk.corpus import cess_esp
bt_spa, cess_test = train_brill_with_corpus(cess_esp)
cesstest1 = [i for i,j in cess_test[0]]
print 'Test sentence:', cesstest1
print bt_spa.tag(cesstest1); print
print bt_spa.evaluate(cess_test)